no code implementations • SIGDIAL (ACL) 2020 • Siqi Shen, Charles Welch, Rada Mihalcea, Verónica Pérez-Rosas
We introduce a counseling dialogue system that seeks to assist counselors while they are learning and refining their counseling skills.
no code implementations • Findings (ACL) 2022 • Yiqun Yao, Rada Mihalcea
Moreover, for different modalities, the best unimodal models may work under significantly different learning rates due to the nature of the modality and the computational flow of the model; thus, selecting a global learning rate for late-fusion models can result in a vanishing gradient for some modalities.
no code implementations • NAACL (CLPsych) 2021 • Do June Min, Verónica Pérez-Rosas, Rada Mihalcea
Automatic speech recognition (ASR) is a crucial step in many natural language processing (NLP) applications, as often available data consists mainly of raw speech.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • ACL 2022 • Charles Welch, Chenxi Gu, Jonathan Kummerfeld, Veronica Perez-Rosas, Rada Mihalcea
Personalized language models are designed and trained to capture language patterns specific to individual users.
no code implementations • COLING (TextGraphs) 2022 • MeiXing Dong, Xueming Xu, Rada Mihalcea
Predicting user behavior is essential for a large number of applications including recommender and dialog systems, and more broadly in domains such as healthcare, education, and economics.
no code implementations • Findings (EMNLP) 2021 • Zhijing Jin, Zeyu Peng, Tejas Vaidhya, Bernhard Schoelkopf, Rada Mihalcea
Mining the causes of political decision-making is an active research area in the field of political science.
1 code implementation • NLPerspectives (LREC) 2022 • Laura Biester, Vanita Sharma, Ashkan Kazemi, Naihao Deng, Steven Wilson, Rada Mihalcea
Recent studies have shown that for subjective annotation tasks, the demographics, lived experiences, and identity of annotators can have a large impact on how items are labeled.
no code implementations • EMNLP 2021 • Md Kamrul Hasan, James Spann, Masum Hasan, Md Saiful Islam, Kurtis Haut, Rada Mihalcea, Ehsan Hoque
The combination of gestures, intonations, and textual content plays a key role in argument delivery.
no code implementations • ACL 2022 • Siqi Shen, Veronica Perez-Rosas, Charles Welch, Soujanya Poria, Rada Mihalcea
We propose a pipeline that collects domain knowledge through web mining, and show that retrieval from both domain-specific and commonsense knowledge bases improves the quality of generated responses.
no code implementations • COLING 2022 • Artem Abzaliev, Andrew Owens, Rada Mihalcea
In this paper, we explore the relation between gestures and language.
no code implementations • COLING 2022 • Santiago Castro, Naihao Deng, Pingxuan Huang, Mihai Burzo, Rada Mihalcea
Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the “in the wild” settings, where the videos are recorded outdoors.
no code implementations • 7 May 2024 • Siqi Shen, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, Soujanya Poria, Rada Mihalcea
Large language models (LLMs) have demonstrated substantial commonsense understanding through numerous benchmark evaluations.
no code implementations • 29 Apr 2024 • Artem Abzaliev, Humberto Pérez Espinosa, Rada Mihalcea
In this paper, we address dog vocalizations and explore the use of self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks that find parallels in human-centered tasks in speech recognition.
no code implementations • 25 Apr 2024 • Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner, Bernhard Schölkopf, Mrinmaya Sachan, Rada Mihalcea
Through this simulation environment, we explore the dynamics of resource sharing among AI agents, highlighting the importance of ethical considerations, strategic planning, and negotiation skills.
1 code implementation • 19 Apr 2024 • Oana Ignat, Gayathri Ganesh Lakshmy, Rada Mihalcea
To this end, we compile and make publicly available the InspAIred dataset, which consists of 2, 000 real inspiring posts, 2, 000 real non-inspiring posts, and 2, 000 generated inspiring posts evenly distributed across India and the UK.
no code implementations • 19 Apr 2024 • Oana Ignat, Xiaomeng Xu, Rada Mihalcea
Using this dataset, we conduct extensive linguistic analyses to (1) compare the AI fake hotel reviews to real hotel reviews, and (2) identify the factors that influence the deception detection model performance.
1 code implementation • 17 Apr 2024 • Zhiheng Lyu, Zhijing Jin, Fernando Gonzalez, Rada Mihalcea, Bernhard Schoelkopf, Mrinmaya Sachan
Sentiment analysis (SA) aims to identify the sentiment expressed in a text, such as a product review.
1 code implementation • 15 Apr 2024 • Navonil Majumder, Chia-Yu Hung, Deepanway Ghosal, Wei-Ning Hsu, Rada Mihalcea, Soujanya Poria
These models do not explicitly focus on the presence of concepts or events and their temporal ordering in the output audio with respect to the input prompt.
no code implementations • 12 Apr 2024 • Siyang Liu, Trish Maturi, Siqi Shen, Rada Mihalcea
In this paper, we explore the alignment of values in Large Language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen categories.
1 code implementation • 25 Mar 2024 • Shinka Mori, Oana Ignat, Andrew Lee, Rada Mihalcea
Using GPT-3, we develop HEADROOM, a synthetic dataset of 3, 120 posts about depression-triggering stressors, by controlling for race, gender, and time frame (before and after COVID-19).
1 code implementation • 20 Mar 2024 • Do June Min, Veronica Perez-Rosas, Kenneth Resnicow, Rada Mihalcea
In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text qualities for natural language generation.
1 code implementation • 12 Mar 2024 • Oana Ignat, Longju Bai, Joan Nwatu, Rada Mihalcea
In this paper, we propose methods to identify the data to be annotated to balance model performance and annotation costs.
1 code implementation • 22 Feb 2024 • Santiago Castro, Amir Ziai, Avneesh Saluja, Zhuoning Yuan, Rada Mihalcea
Recent years have witnessed a significant increase in the performance of Vision and Language tasks.
no code implementations • 20 Feb 2024 • Hanchen Xia, Feng Jiang, Naihao Deng, Cunxiang Wang, Guojiang Zhao, Rada Mihalcea, Yue Zhang
Modern LLMs have become increasingly powerful, but they are still facing challenges in specialized tasks such as Text-to-SQL.
1 code implementation • 19 Feb 2024 • Sahand Sabour, Siyang Liu, Zheyuan Zhang, June M. Liu, Jinfeng Zhou, Alvionna S. Sunaryo, Juanzi Li, Tatia M. C. Lee, Rada Mihalcea, Minlie Huang
Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks.
no code implementations • 19 Feb 2024 • Naihao Deng, Zhenjie Sun, Ruiqi He, Aman Sikka, Yulong Chen, Lin Ma, Yue Zhang, Rada Mihalcea
In this paper, we investigate the effectiveness of various LLMs in interpreting tabular data through different prompting strategies and data formats.
1 code implementation • 17 Jan 2024 • Pengfei Hong, Deepanway Ghosal, Navonil Majumder, Somak Aditya, Rada Mihalcea, Soujanya Poria
Recent advancements in Large Language Models (LLMs) have showcased striking results on existing logical reasoning benchmarks, with some models even surpassing human performance.
no code implementations • 10 Jan 2024 • Ian Stewart, Rada Mihalcea
Machine translation often suffers from biased data and algorithms that can lead to unacceptable errors in system output.
1 code implementation • 3 Jan 2024 • Andrew Lee, Xiaoyan Bai, Itamar Pres, Martin Wattenberg, Jonathan K. Kummerfeld, Rada Mihalcea
While alignment algorithms are now commonly used to tune pre-trained language models towards a user's preferences, we lack explanations for the underlying mechanisms in which models become ``aligned'', thus making it difficult to explain phenomena like jailbreaks.
no code implementations • 18 Nov 2023 • Panfeng Li, Mohamed Abouelenien, Rada Mihalcea
This paper explores the application of convolutional neural networks for the purpose of multimodal deception detection.
no code implementations • 14 Nov 2023 • Do June Min, Verónica Pérez-Rosas, Kenneth Resnicow, Rada Mihalcea
We introduce VERVE, a template-based rewriting system with paraphrase-augmented training and adaptive template updating.
1 code implementation • 9 Nov 2023 • Joan Nwatu, Oana Ignat, Rada Mihalcea
Despite the impressive performance of current AI models reported across various tasks, performance reports often do not include evaluations of how these models perform on the specific groups that will be impacted by these technologies.
1 code implementation • 31 Oct 2023 • Deepanway Ghosal, Navonil Majumder, Roy Ka-Wei Lee, Rada Mihalcea, Soujanya Poria
Visual question answering (VQA) is the task of answering questions about an image.
1 code implementation • 25 Oct 2023 • Yinghui He, Yufan Wu, Yilin Jia, Rada Mihalcea, Yulong Chen, Naihao Deng
Theory of Mind (ToM) is the ability to reason about one's own and others' mental states.
no code implementations • 9 Oct 2023 • Siyang Liu, Naihao Deng, Sahand Sabour, Yilin Jia, Minlie Huang, Rada Mihalcea
We propose task-adaptive tokenization as a way to adapt the generation pipeline to the specifics of a downstream task and enhance long-form generation in mental health.
1 code implementation • 12 Sep 2023 • Oana Ignat, Santiago Castro, Weiji Li, Rada Mihalcea
We create and make publicly available the ACE (Action Co-occurrencE) dataset, consisting of a large graph of ~12k co-occurring pairs of visual actions and their corresponding video clips.
no code implementations • 21 Jun 2023 • Ashkan Kazemi, Rada Mihalcea
Social media feed algorithms are designed to optimize online social engagements for the purpose of maximizing advertising profits, and therefore have an incentive to promote controversial posts including misinformation.
1 code implementation • 9 Jun 2023 • Zhijing Jin, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona Diab, Bernhard Schölkopf
In this work, we propose the first benchmark dataset to test the pure causal inference skills of large language models (LLMs).
1 code implementation • 30 May 2023 • Santiago Castro, Oana Ignat, Rada Mihalcea
Joint vision-language models have shown great performance over a diverse set of tasks.
1 code implementation • 24 May 2023 • Yiwen Ding, Jiarui Liu, Zhiheng Lyu, Kun Zhang, Bernhard Schoelkopf, Zhijing Jin, Rada Mihalcea
While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topics and different development trends.
1 code implementation • 24 May 2023 • Naihao Deng, Xinliang Frederick Zhang, Siyang Liu, Winston Wu, Lu Wang, Rada Mihalcea
Annotator disagreement is ubiquitous in natural language processing (NLP) tasks.
no code implementations • 23 May 2023 • Naihao Deng, YiKai Liu, Mingye Chen, Winston Wu, Siyang Liu, Yulong Chen, Yue Zhang, Rada Mihalcea
Our results show that our system can meet the diverse needs of NLP researchers and significantly accelerate the annotation process.
no code implementations • 21 May 2023 • Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
Not surprisingly, this has, in turn, made many NLP researchers -- especially those at the beginning of their careers -- worry about what NLP research area they should focus on.
1 code implementation • 9 May 2023 • Fernando Gonzalez, Zhijing Jin, Bernhard Schölkopf, Tom Hope, Mrinmaya Sachan, Rada Mihalcea
Using state-of-the-art NLP models, we address each of these tasks and use them on the entire ACL Anthology, resulting in a visualization workspace that gives researchers a comprehensive overview of the field of NLP4SG.
1 code implementation • 2 May 2023 • Zhiheng Lyu, Zhijing Jin, Justus Mattern, Rada Mihalcea, Mrinmaya Sachan, Bernhard Schoelkopf
In this work, we take sentiment classification as an example and look into the causal relations between the review (X) and sentiment (Y).
no code implementations • 30 Apr 2023 • Ambuj Mehrish, Navonil Majumder, Rishabh Bhardwaj, Rada Mihalcea, Soujanya Poria
The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications.
1 code implementation • 2 Mar 2023 • Yingting Li, Ambuj Mehrish, Shuai Zhao, Rishabh Bhardwaj, Amir Zadeh, Navonil Majumder, Rada Mihalcea, Soujanya Poria
To mitigate this issue, parameter-efficient transfer learning algorithms, such as adapters and prefix tuning, have been proposed as a way to introduce a few trainable parameters that can be plugged into large pre-trained language models such as BERT, and HuBERT.
no code implementations • 7 Feb 2023 • Zhijing Jin, Rada Mihalcea
This text is from Chapter 7 (pages 141-162) of the Handbook of Computational Social Science for Policy (2023).
no code implementations • 20 Dec 2022 • Justus Mattern, Zhijing Jin, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schölkopf
Generated texts from large pretrained language models have been shown to exhibit a variety of harmful, human-like biases about various demographics.
1 code implementation • 5 Dec 2022 • Anna Costello, Ekaterina Fedorova, Zhijing Jin, Rada Mihalcea
However, when we trace those early drafts to their published versions, a substantial gender gap in linguistic uncertainty arises.
1 code implementation • 29 Oct 2022 • Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria
We show the efficacy of our proposed approach in different tasks -- abductive reasoning, commonsense question answering, science question answering, and sentence completion.
Ranked #2 on Sentence Completion on HellaSwag
no code implementations • 14 Oct 2022 • Ashkan Kazemi, Artem Abzaliev, Naihao Deng, Rui Hou, Scott A. Hale, Verónica Pérez-Rosas, Rada Mihalcea
We propose a novel system to help fact-checkers formulate search queries for known misinformation claims and effectively search across multiple social media platforms.
1 code implementation • 6 Oct 2022 • Siqi Shen, Deepanway Ghosal, Navonil Majumder, Henry Lim, Rada Mihalcea, Soujanya Poria
Our results show that the proposed pre-training objectives are effective at adapting the pre-trained T5-Large model for the contextual commonsense inference task.
Ranked #1 on Multiview Contextual Commonsense Inference on CICERO (using extra training data)
1 code implementation • 4 Oct 2022 • Zhijing Jin, Sydney Levine, Fernando Gonzalez, Ojasv Kamal, Maarten Sap, Mrinmaya Sachan, Rada Mihalcea, Josh Tenenbaum, Bernhard Schölkopf
Using a state-of-the-art large language model (LLM) as a basis, we propose a novel moral chain of thought (MORALCOT) prompting strategy that combines the strengths of LLMs with theories of moral reasoning developed in cognitive science to predict human moral judgments.
no code implementations • 14 Sep 2022 • Santiago Castro, Naihao Deng, Pingxuan Huang, Mihai Burzo, Rada Mihalcea
Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the "in the wild" settings, where the videos are recorded outdoors.
Ranked #1 on Video Question Answering on WildQA
no code implementations • 23 Aug 2022 • MeiXing Dong, Ruixuan Sun, Laura Biester, Rada Mihalcea
Notably, we find that communities that talked more about social ties normally experienced in-person, such as friends, family, and affiliations, were actually more likely to be impacted.
no code implementations • NAACL 2022 • Laura Burdick, Jonathan K. Kummerfeld, Rada Mihalcea
We use paraphrases as a unique source of data to analyze contextualized embeddings, with a particular focus on BERT.
1 code implementation • ACL 2022 • Deepanway Ghosal, Siqi Shen, Navonil Majumder, Rada Mihalcea, Soujanya Poria
This paper addresses the problem of dialogue reasoning with contextualized commonsense inference.
Ranked #1 on Answer Generation on CICERO
2 code implementations • 28 Feb 2022 • Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schölkopf
In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate).
1 code implementation • 16 Feb 2022 • Oana Ignat, Santiago Castro, YuHang Zhou, Jiajun Bao, Dandan Shan, Rada Mihalcea
We consider the task of temporal human action localization in lifestyle vlogs.
no code implementations • 14 Feb 2022 • Ashkan Kazemi, Zehua Li, Verónica Pérez-Rosas, Scott A. Hale, Rada Mihalcea
We conduct both classification and retrieval experiments, in monolingual (English only), multilingual (Spanish, Portuguese), and cross-lingual (Hindi-English) settings using multilingual transformer models such as XLM-RoBERTa and multilingual embeddings such as LaBSE and SBERT.
no code implementations • SIGDIAL (ACL) 2022 • Ian Stewart, Rada Mihalcea
We find that different social groups, such as experts and novices, consistently ask different types of questions.
1 code implementation • Findings (EMNLP) 2021 • Andrew Lee, Jonathan K. Kummerfeld, Lawrence C. An, Rada Mihalcea
Many statistical models have high accuracy on test benchmarks, but are not explainable, struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily integrate domain expertise.
1 code implementation • EMNLP 2021 • Oana Ignat, Santiago Castro, Hanwen Miao, Weiji Li, Rada Mihalcea
We aim to automatically identify human action reasons in online videos.
1 code implementation • EMNLP 2021 • Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria
Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document.
1 code implementation • Findings (ACL) 2021 • Allison Lahnala, Yuntian Zhao, Charles Welch, Jonathan K. Kummerfeld, Lawrence An, Kenneth Resnicow, Rada Mihalcea, Verónica Pérez-Rosas
A growing number of people engage in online health forums, making it important to understand the quality of the advice they receive.
1 code implementation • 22 Jun 2021 • Navonil Majumder, Deepanway Ghosal, Devamanyu Hazarika, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria
We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics.
2 code implementations • Findings (ACL) 2021 • Zhijing Jin, Geeticka Chauhan, Brian Tse, Mrinmaya Sachan, Rada Mihalcea
We lay the foundations via the moral philosophy definition of social good, propose a framework to evaluate the direct and indirect real-world impact of NLP tasks, and adopt the methodology of global priorities research to identify priority causes for NLP research.
1 code implementation • SIGDIAL (ACL) 2021 • Deepanway Ghosal, Pengfei Hong, Siqi Shen, Navonil Majumder, Rada Mihalcea, Soujanya Poria
Commonsense inference to understand and explain human language is a fundamental research problem in natural language processing.
no code implementations • NAACL 2021 • Yiqun Yao, Michalis Papakostas, Mihai Burzo, Mohamed Abouelenien, Rada Mihalcea
The capability to automatically detect human stress can benefit artificial intelligent agents involved in affective computing and human-computer interaction.
no code implementations • NAACL (SocialNLP) 2021 • MeiXing Dong, Xueming Xu, Yiwei Zhang, Ian Stewart, Rada Mihalcea
Many people aim for change, but not everyone succeeds.
no code implementations • NAACL (NLP4IF) 2021 • Ashkan Kazemi, Zehua Li, Verónica Pérez-Rosas, Rada Mihalcea
In this paper, we explore the construction of natural language explanations for news claims, with the goal of assisting fact-checking and news evaluation applications.
1 code implementation • ACL 2022 • Santiago Castro, Ruoyao Wang, Pingxuan Huang, Ian Stewart, Oana Ignat, Nan Liu, Jonathan C. Stroud, Rada Mihalcea
We propose fill-in-the-blanks as a video understanding evaluation framework and introduce FIBER -- a novel dataset consisting of 28, 000 videos and descriptions in support of this evaluation framework.
no code implementations • 4 Feb 2021 • Allison Lahnala, Gauri Kambhatla, Jiajun Peng, Matthew Whitehead, Gillian Minnehan, Eric Guldan, Jonathan K. Kummerfeld, Anıl Çamcı, Rada Mihalcea
In the first case study, we demonstrate that using chord embeddings in a next chord prediction task yields predictions that more closely match those by experienced musicians.
no code implementations • 25 Jan 2021 • Thamar Solorio, Mahsa Shafaei, Christos Smailis, Mona Diab, Theodore Giannakopoulos, Heng Ji, Yang Liu, Rada Mihalcea, Smaranda Muresan, Ioannis Kakadiaris
This white paper presents a summary of the discussions regarding critical considerations to develop an extensive repository of online videos annotated with labels indicating questionable content.
1 code implementation • 22 Dec 2020 • Soujanya Poria, Navonil Majumder, Devamanyu Hazarika, Deepanway Ghosal, Rishabh Bhardwaj, Samson Yu Bai Jian, Pengfei Hong, Romila Ghosh, Abhinaba Roy, Niyati Chhaya, Alexander Gelbukh, Rada Mihalcea
We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong Transformer-based baselines.
Ranked #1 on Recognizing Emotion Cause in Conversations on RECCON
no code implementations • 11 Dec 2020 • Abhinaba Roy, Deepanway Ghosal, Erik Cambria, Navonil Majumder, Rada Mihalcea, Soujanya Poria
Zero shot learning -- the problem of training and testing on a completely disjoint set of classes -- relies greatly on its ability to transfer knowledge from train classes to test classes.
no code implementations • COLING 2020 • Aparna Garimella, Carmen Banea, Nabil Hossain, Rada Mihalcea
The subjective nature of humor makes computerized humor generation a challenging task.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Laura Biester, Carmen Banea, Rada Mihalcea
Word embedding methods have become the de-facto way to represent words, having been successfully applied to a wide array of natural language processing tasks.
no code implementations • COLING 2020 • Charles Welch, Jonathan K. Kummerfeld, Verónica Pérez-Rosas, Rada Mihalcea
Our results show that a subset of words belonging to specific psycholinguistic categories tend to vary more in their representations across users and that combining generic and personalized word embeddings yields the best performance, with a 4. 7% relative reduction in perplexity.
no code implementations • COLING 2020 • Ashkan Kazemi, Verónica Pérez-Rosas, Rada Mihalcea
We introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to their relevance to an input "focus."
2 code implementations • CL (ACL) 2022 • Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea
Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others.
1 code implementation • EMNLP 2020 • Charles Welch, Jonathan K. Kummerfeld, Verónica Pérez-Rosas, Rada Mihalcea
Word embeddings are usually derived from corpora containing text from many individuals, thus leading to general purpose representations rather than individually personalized representations.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Deepanway Ghosal, Navonil Majumder, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria
In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge.
Ranked #11 on Emotion Recognition in Conversation on DailyDialog
1 code implementation • EMNLP 2020 • Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria
Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly.
1 code implementation • EMNLP 2020 • Charles Welch, Rada Mihalcea, Jonathan K. Kummerfeld
In the process, we show that the standard convention of tying input and output embeddings does not improve perplexity when initializing with embeddings trained on in-domain data.
2 code implementations • 29 Sep 2020 • Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria
Most of these approaches account for the context for effective understanding.
no code implementations • EMNLP (NLP-COVID19) 2020 • Laura Biester, Katie Matton, Janarthanan Rajendran, Emily Mower Provost, Rada Mihalcea
The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health.
no code implementations • EMNLP (NLP-COVID19) 2020 • Charles Welch, Allison Lahnala, Verónica Pérez-Rosas, Siqi Shen, Sarah Seraj, Larry An, Kenneth Resnicow, James Pennebaker, Rada Mihalcea
The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability.
no code implementations • 31 May 2020 • Aparna Garimella, Carmen Banea, Nabil Hossain, Rada Mihalcea
The subjective nature of humor makes computerized humor generation a challenging task.
1 code implementation • ACL 2020 • Deepanway Ghosal, Devamanyu Hazarika, Abhinaba Roy, Navonil Majumder, Rada Mihalcea, Soujanya Poria
Cross-domain sentiment analysis has received significant attention in recent years, prompted by the need to combat the domain gap between different applications that make use of sentiment analysis.
no code implementations • LREC 2020 • Mimansa Jaiswal, Cristian-Paul Bara, Yuanhang Luo, Mihai Burzo, Rada Mihalcea, Emily Mower Provost
Endowing automated agents with the ability to provide support, entertainment and interaction with human beings requires sensing of the users{'} affective state.
no code implementations • LREC 2020 • Zhentao Xu, Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea
In this paper, we explore the use of multimodal cues present in social media posts to predict users{'} mental health status.
no code implementations • LREC 2020 • Amy Rechkemmer, Steven Wilson, Rada Mihalcea
Using a set of over 2 million posts from distinct Twitter users around the country dating back as far as 2014, we ask the following question: is there a difference in how Americans express themselves online depending on whether they reside in an urban or rural area?
1 code implementation • LREC 2020 • Santiago Castro, Mahmoud Azab, Jonathan Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, Rada Mihalcea
We introduce LifeQA, a benchmark dataset for video question answering that focuses on day-to-day real-life situations.
1 code implementation • 1 May 2020 • Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Rada Mihalcea
Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago.
1 code implementation • EMNLP 2021 • Laura Burdick, Jonathan K. Kummerfeld, Rada Mihalcea
Word embeddings are powerful representations that form the foundation of many natural language processing architectures, both in English and in other languages.
no code implementations • 4 Dec 2019 • Jonathan C. Stroud, Ryan McCaffrey, Rada Mihalcea, Jia Deng, Olga Russakovsky
Temporal grounding entails establishing a correspondence between natural language event descriptions and their visual depictions.
no code implementations • IJCNLP 2019 • Mahmoud Azab, Stephane Dadian, Vivi Nastase, Larry An, Rada Mihalcea
We introduce a new dataset consisting of natural language interactions annotated with medical family histories, obtained during interactions with a genetic counselor and through crowdsourcing, following a questionnaire created by experts in the domain.
no code implementations • CONLL 2019 • Mahmoud Azab, Noriyuki Kojima, Jia Deng, Rada Mihalcea
We introduce a new embedding model to represent movie characters and their interactions in a dialogue by encoding in the same representation the language used by these characters as well as information about the other participants in the dialogue.
1 code implementation • 11 Oct 2019 • Devamanyu Hazarika, Soujanya Poria, Roger Zimmermann, Rada Mihalcea
We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target).
Ranked #19 on Emotion Recognition in Conversation on DailyDialog
no code implementations • 4 Sep 2019 • Rui Hou, Verónica Pérez-Rosas, Stacy Loeb, Rada Mihalcea
Recent years have witnessed a significant increase in the online sharing of medical information, with videos representing a large fraction of such online sources.
no code implementations • 13 Aug 2019 • Navonil Majumder, Soujanya Poria, Gangeshwar Krishnamurthy, Niyati Chhaya, Rada Mihalcea, Alexander Gelbukh
Multimodal fusion is considered a key step in multimodal tasks such as sentiment analysis, emotion detection, question answering, and others.
no code implementations • ACL 2019 • Steven R. Wilson, Rada Mihalcea
The activities we do are linked to our interests, personality, political preferences, and decisions we make about the future.
no code implementations • ACL 2019 • Ver{\'o}nica P{\'e}rez-Rosas, Xinyi Wu, Kenneth Resnicow, Rada Mihalcea
Our results suggest important language differences in low- and high-quality counseling, which we further use to derive linguistic features able to capture the differences between the two groups.
1 code implementation • ACL 2019 • Santiago Castro, Devamanyu Hazarika, Ver{\'o}nica P{\'e}rez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria
As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows.
no code implementations • ACL 2019 • Aparna Garimella, Carmen Banea, Dirk Hovy, Rada Mihalcea
Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for these differences.
1 code implementation • ACL 2019 • Oana Ignat, Laura Burdick, Jia Deng, Rada Mihalcea
We consider the task of identifying human actions visible in online videos.
1 code implementation • 5 Jun 2019 • Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria
As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows.
no code implementations • NAACL 2019 • Felix Soldner, Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea
Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection.
1 code implementation • 8 May 2019 • Soujanya Poria, Navonil Majumder, Rada Mihalcea, Eduard Hovy
Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI).
Ranked #6 on Emotion Recognition in Conversation on EC
1 code implementation • 25 Apr 2019 • Charles Welch, Verónica Pérez-Rosas, Jonathan K. Kummerfeld, Rada Mihalcea
We examine a large dialog corpus obtained from the conversation history of a single individual with 104 conversation partners.
no code implementations • 27 Mar 2019 • Mimansa Jaiswal, Zakaria Aldeneh, Cristian-Paul Bara, Yuanhang Luo, Mihai Burzo, Rada Mihalcea, Emily Mower Provost
As a result, annotations are colored by the manner in which they were collected.
no code implementations • 19 Nov 2018 • Konstantinos Pappas, Mahmoud Azab, Rada Mihalcea
The geolocation of online information is an essential component in any geospatial application.
2 code implementations • 1 Nov 2018 • Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, Erik Cambria
Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc.
Ranked #3 on Emotion Recognition in Conversation on SEMAINE
Emotion Classification Emotion Recognition in Conversation +2
8 code implementations • ACL 2019 • Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, Rada Mihalcea
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
1 code implementation • EMNLP 2018 • Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, Erik Cambria, Roger Zimmermann
Emotion recognition in conversations is crucial for building empathetic machines.
Ranked #50 on Emotion Recognition in Conversation on IEMOCAP
Emotion Recognition in Conversation General Classification +2
no code implementations • NAACL 2018 • Mahmoud Azab, Mingzhe Wang, Max Smith, Noriyuki Kojima, Jia Deng, Rada Mihalcea
We propose a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework.
no code implementations • SEMEVAL 2019 • Li Zhang, Steven R. Wilson, Rada Mihalcea
Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e. g., similarity, relatedness, and so on.
1 code implementation • COLING 2018 • Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, Rada Mihalcea
The literature in automated sarcasm detection has mainly focused on lexical, syntactic and semantic-level analysis of text.
Ranked #1 on Sarcasm Detection on SARC (all-bal)
2 code implementations • NAACL 2018 • Laura Wendlandt, Jonathan K. Kummerfeld, Rada Mihalcea
Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations.
no code implementations • 20 Apr 2018 • Li Zhang, Steven R. Wilson, Rada Mihalcea
Sentence encoders, which produce sentence embeddings using neural networks, are typically evaluated by how well they transfer to downstream tasks.
no code implementations • IJCNLP 2017 • Ver{\'o}nica P{\'e}rez-Rosas, Quincy Davenport, Anna Mengdan Dai, Mohamed Abouelenien, Rada Mihalcea
This paper addresses the task of detecting identity deception in language.
no code implementations • IJCNLP 2017 • Steven Wilson, Rada Mihalcea
The things people do in their daily lives can provide valuable insights into their personality, values, and interests.
no code implementations • IJCNLP 2017 • Shibamouli Lahiri, V.G.Vinod Vydiswaran, Rada Mihalcea
The system combines lexical, syntactic, and semantic features in a product-agnostic fashion to yield good classification performance.
no code implementations • EMNLP 2017 • Aparna Garimella, Carmen Banea, Rada Mihalcea
Variations of word associations across different groups of people can provide insights into people{'}s psychologies and their world views.
no code implementations • COLING 2018 • Verónica Pérez-Rosas, Bennett Kleinberg, Alexandra Lefevre, Rada Mihalcea
The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content.
no code implementations • ACL 2017 • Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence An
Counselor empathy is associated with better outcomes in psychology and behavioral counseling.
no code implementations • EACL 2017 • Carlo Strapparava, Rada Mihalcea
We present a computational analysis of the language of drug users when talking about their drug experiences.
no code implementations • EACL 2017 • Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence An, Kathy J. Goggin, Delwyn Catley
As the number of people receiving psycho-therapeutic treatment increases, the automatic evaluation of counseling practice arises as an important challenge in the clinical domain.
no code implementations • 24 Dec 2016 • Konstantinos Pappas, Rada Mihalcea
Automatic profiling of social media users is an important task for supporting a multitude of downstream applications.
no code implementations • 20 Dec 2016 • Konstantinos Pappas, Steven Wilson, Rada Mihalcea
People's personality and motivations are manifest in their everyday language usage.
no code implementations • COLING 2016 • Aparna Garimella, Rada Mihalcea, James Pennebaker
Personal writings have inspired researchers in the fields of linguistics and psychology to study the relationship between language and culture to better understand the psychology of people across different cultures.
no code implementations • COLING 2016 • Charles Welch, Rada Mihalcea
We address the task of targeted sentiment as a means of understanding the sentiment that students hold toward courses and instructors, as expressed by students in their comments.
no code implementations • WS 2016 • Aparna Garimella, Rada Mihalcea
Men are from Mars and women are from Venus - or so the genre of relationship literature would have us believe.
no code implementations • LREC 2016 • Carmen Banea, Xi Chen, Rada Mihalcea
Just as industrialization matured from mass production to customization and personalization, so has the Web migrated from generic content to public disclosures of one{'}s most intimately held thoughts, opinions and beliefs.
no code implementations • CVPR 2015 • Yu-Wei Chao, Zhan Wang, Rada Mihalcea, Jia Deng
In this paper we introduce the new problem of mining the knowledge of semantic affordance: given an object, determining whether an action can be performed on it.
no code implementations • SEMEVAL 2015 • Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, I{\~n}igo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, Janyce Wiebe
no code implementations • LREC 2014 • Vanessa Loza, Shibamouli Lahiri, Rada Mihalcea, Po-Hsiang Lai
This paper introduces a new email dataset, consisting of both single and thread emails, manually annotated with summaries and keywords.
no code implementations • LREC 2014 • Chris Hokamp, Rada Mihalcea, Peter Schuelke
We describe the results of several experiments with interactive interfaces for native and L2 English students, designed to collect implicit feedback from students as they complete a reading activity.
no code implementations • LREC 2014 • Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea, Alexis Narvaez, Mihai Burzo
This paper presents the construction of a multimodal dataset for deception detection, including physiological, thermal, and visual responses of human subjects under three deceptive scenarios.
no code implementations • 12 Nov 2013 • Shibamouli Lahiri, Rada Mihalcea
The goal of our paper is to explore properties of these complex networks that are suitable as features for machine-learning-based authorship attribution of documents.
no code implementations • LREC 2012 • Ver{\'o}nica P{\'e}rez-Rosas, Carmen Banea, Rada Mihalcea
In this paper we present a framework to derive sentiment lexicons in a target language by using manually or automatically annotated data available in an electronic resource rich language, such as English.
no code implementations • LREC 2012 • Fern, Erwin ez-Ordo{\~n}ez, Rada Mihalcea, Samer Hassan
In this paper we investigate the role of multilingual features in improving word sense disambiguation.
no code implementations • LREC 2012 • Carlo Strapparava, Rada Mihalcea, Alberto Battocchi
In this paper, we introduce a novel parallel corpus of music and lyrics, annotated with emotions at line level.
1 code implementation • Conference 2004 • Rada Mihalcea, Paul Tarau
In this paper, we introduce TextRank – a graph-based ranking model for text processing and show how this model can be successfully used in natural language applications.