no code implementations • 19 Feb 2024 • Jonathan Zheng, Alan Ritter, Wei Xu
The performance of Large Language Models (LLMs) degrades from the temporal drift between data used for model training and newer text seen during inference.
no code implementations • 6 Feb 2024 • Anton Lavrouk, Ian Ligon, Tarek Naous, Jonathan Zheng, Alan Ritter, Wei Xu
The Stanceosaurus corpus (Zheng et al., 2022) was designed to provide high-quality, annotated, 5-way stance data extracted from Twitter, suitable for analyzing cross-cultural and cross-lingual misinformation.
1 code implementation • 5 Feb 2024 • Duong Minh Le, Yang Chen, Alan Ritter, Wei Xu
Therefore, it is common to exploit translation and label projection to further improve the performance by (1) translating training data that is available in a high-resource language (e. g., English) together with the gold labels into low-resource languages, and/or (2) translating test data in low-resource languages to a high-source language to run inference on, then projecting the predicted span-level labels back onto the original test data.
no code implementations • 28 Nov 2023 • Cong Wei, Yang Chen, Haonan Chen, Hexiang Hu, Ge Zhang, Jie Fu, Alan Ritter, Wenhu Chen
Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text descriptions, searching for a news article with a headline image, or finding a similar photo with a query image.
no code implementations • 16 Nov 2023 • Yao Dou, Isadora Krsek, Tarek Naous, Anubha Kabra, Sauvik Das, Alan Ritter, Wei Xu
Motivated by the user feedback, we introduce the task of self-disclosure abstraction, which is paraphrasing disclosures into less specific terms while preserving their utility, e. g., "Im 16F" to "I'm a teenage girl".
no code implementations • 2 Nov 2023 • Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, Stuart Russell
Our benchmark results show that many models are vulnerable to the attack strategies in the Tensor Trust dataset.
no code implementations • 3 Oct 2023 • Yang Chen, Ethan Mendes, Sauvik Das, Wei Xu, Alan Ritter
While data leaks should be prevented, it is also crucial to examine the trade-off between the privacy protection and model utility of proposed approaches.
no code implementations • 29 Sep 2023 • Junmo Kang, Hongyin Luo, Yada Zhu, James Glass, David Cox, Alan Ritter, Rogerio Feris, Leonid Karlinsky
Recent works have demonstrated the effectiveness of self-alignment in which a large language model is, by itself, aligned to follow general instructions through the automatic generation of instructional data using a handful of human-written seeds.
1 code implementation • 26 May 2023 • Duong Minh Le, Ruohao Guo, Wei Xu, Alan Ritter
In this paper, we study the task of instructional dialogue and focus on the cooking domain.
no code implementations • 24 May 2023 • Ruohao Guo, Wei Xu, Alan Ritter
Style is used to convey authors' intentions and attitudes.
no code implementations • 23 May 2023 • Tarek Naous, Michael J. Ryan, Alan Ritter, Wei Xu
In this paper, we show that multilingual and Arabic monolingual LMs exhibit bias towards entities associated with Western culture.
1 code implementation • 23 May 2023 • Fan Bai, Junmo Kang, Gabriel Stanovsky, Dayne Freitag, Alan Ritter
We use this collection of annotated tables to evaluate the ability of open-source and API-based language models to extract information from tables covering diverse domains and data formats.
Ranked #1 on Attribute Extraction on SWDE
1 code implementation • 23 May 2023 • Yang Chen, Vedaant Shah, Alan Ritter
Pre-trained multilingual language models have enabled significant advancements in cross-lingual transfer.
Cross-Lingual Transfer Low Resource Named Entity Recognition +4
1 code implementation • 23 May 2023 • Nghia T. Le, Alan Ritter
Recent work on extending coreference resolution across domains and languages relies on annotated data in both the target domain and language.
no code implementations • 2 May 2023 • Junmo Kang, Wei Xu, Alan Ritter
Fine-tuning large models is highly effective, however, inference can be expensive and produces carbon emissions.
2 code implementations • 23 Feb 2023 • Yang Chen, Hexiang Hu, Yi Luan, Haitian Sun, Soravit Changpinyo, Alan Ritter, Ming-Wei Chang
In this study, we introduce InfoSeek, a visual question answering dataset tailored for information-seeking questions that cannot be answered with only common sense knowledge.
Ranked #3 on Visual Question Answering (VQA) on InfoSeek
1 code implementation • 19 Dec 2022 • Ethan Mendes, Yang Chen, Wei Xu, Alan Ritter
We present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them.
1 code implementation • 19 Dec 2022 • Shuheng Liu, Alan Ritter
In this paper, we evaluate the generalization of over 20 different models trained on CoNLL-2003, and show that NER models have very different generalization.
1 code implementation • 28 Nov 2022 • Yang Chen, Chao Jiang, Alan Ritter, Wei Xu
Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer.
Ranked #1 on Cross-Lingual NER on MasakhaNER2.0
no code implementations • 28 Oct 2022 • Jonathan Zheng, Ashutosh Baheti, Tarek Naous, Wei Xu, Alan Ritter
We present Stanceosaurus, a new corpus of 28, 033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims.
1 code implementation • 7 Oct 2022 • Nghia T. Le, Fan Bai, Alan Ritter
As far as we are aware, this is the first work to present experimental results demonstrating the effectiveness of in-context learning on the task of few-shot anaphora resolution in scientific protocols.
1 code implementation • 15 Aug 2022 • Fan Bai, Alan Ritter, Peter Madrid, Dayne Freitag, John Niekrasz
In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols.
1 code implementation • EMNLP 2021 • Fan Bai, Alan Ritter, Wei Xu
Our experiments suggest task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain.
1 code implementation • EMNLP 2021 • Ashutosh Baheti, Maarten Sap, Alan Ritter, Mark Riedl
To better understand the dynamics of contextually offensive language, we investigate the stance of dialogue model responses in offensive Reddit conversations.
2 code implementations • EACL 2021 • Ronen Tamari, Fan Bai, Alan Ritter, Gabriel Stanovsky
We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, long-range coreference, grounding, and implicit arguments.
1 code implementation • EMNLP (WNUT) 2020 • Jeniya Tabassum, Sydney Lee, Wei Xu, Alan Ritter
This paper presents the results of the wet lab information extraction task at WNUT 2020.
Ranked #1 on Relation Extraction on WNUT 2020
1 code implementation • EMNLP 2021 • Yang Chen, Alan Ritter
Transformers that are pre-trained on multilingual corpora, such as, mBERT and XLM-RoBERTa, have achieved impressive cross-lingual transfer capabilities.
no code implementations • 28 Sep 2020 • Yang Chen, Alan Ritter
In the zero-shot cross-lingual transfer setting, only English training data is assumed, and the fine-tuned model is evaluated on another target language.
1 code implementation • ACL 2020 • Shi Zong, Alan Ritter, Eduard Hovy
We present a number of linguistic metrics which are computed over text associated with people's predictions about the future including: uncertainty, readability, and emotion.
2 code implementations • COLING 2022 • Shi Zong, Ashutosh Baheti, Wei Xu, Alan Ritter
In this paper, we present a manually annotated corpus of 10, 000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, claimed cures and preventions.
1 code implementation • ACL 2020 • Ashutosh Baheti, Alan Ritter, Kevin Small
In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness.
2 code implementations • ACL 2020 • Jeniya Tabassum, Mounica Maddela, Wei Xu, Alan Ritter
We also present the SoftNER model which achieves an overall 79. 10 F$_1$ score for code and named entity recognition on StackOverflow data.
1 code implementation • EMNLP 2020 • Wuwei Lan, Yang Chen, Wei Xu, Alan Ritter
Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable the effective cross-lingual zero-shot transfer.
no code implementations • SEMEVAL 2013 • Preslav Nakov, Zornitsa Kozareva, Alan Ritter, Sara Rosenthal, Veselin Stoyanov, Theresa Wilson
To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a message-level subtask.
no code implementations • SEMEVAL 2014 • Sara Rosenthal, Preslav Nakov, Alan Ritter, Veselin Stoyanov
We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014.
no code implementations • SEMEVAL 2015 • Sara Rosenthal, Saif M. Mohammad, Preslav Nakov, Alan Ritter, Svetlana Kiritchenko, Veselin Stoyanov
In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter.
no code implementations • SEMEVAL 2016 • Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, Veselin Stoyanov
The three new subtasks focus on two variants of the basic ``sentiment classification in Twitter'' task.
1 code implementation • NAACL 2019 • Fan Bai, Alan Ritter
Our approach achieves state-of-the-art results on minimally supervised sentential relation extraction, outperforming a number of baselines, including a competitive approach that uses the attention layer of a purely neural model.
1 code implementation • NAACL 2019 • Shi Zong, Alan Ritter, Graham Mueller, Evan Wright
In this paper, we investigate methods to analyze the severity of cybersecurity threats based on the language that is used to describe them online.
2 code implementations • EMNLP 2018 • Ashutosh Baheti, Alan Ritter, Jiwei Li, Bill Dolan
Neural conversation models tend to generate safe, generic responses for most inputs.
no code implementations • NAACL 2018 • Chaitanya Kulkarni, Wei Xu, Alan Ritter, Raghu Machiraju
We make our annotated Wet Lab Protocol Corpus available to the research community.
no code implementations • EMNLP 2017 • S Swamy, esh, Alan Ritter, Marie-Catherine de Marneffe
Social media users often make explicit predictions about upcoming events.
1 code implementation • EMNLP 2017 • Sandesh Swamy, Alan Ritter, Marie-Catherine de Marneffe
Social media users often make explicit predictions about upcoming events.
8 code implementations • EMNLP 2017 • Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, Dan Jurafsky
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.
Ranked #1 on Dialogue Generation on Amazon-5
no code implementations • WS 2016 • Benjamin Strauss, Bethany Toma, Alan Ritter, Marie-Catherine de Marneffe, Wei Xu
This paper presents the results of the Twitter Named Entity Recognition shared task associated with W-NUT 2016: a named entity tagging task with 10 teams participating.
1 code implementation • 9 Aug 2016 • Jeniya Tabassum, Alan Ritter, Wei Xu
We describe TweeTIME, a temporal tagger for recognizing and normalizing time expressions in Twitter.
8 code implementations • EMNLP 2016 • Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky
Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes.
no code implementations • 18 Oct 2015 • Jiwei Li, Alan Ritter, Dan Jurafsky
Inferring latent attributes of people online is an important social computing task, but requires integrating the many heterogeneous sources of information available on the web.
no code implementations • CVPR 2015 • Xinlei Chen, Alan Ritter, Abhinav Gupta, Tom Mitchell
We present a co-clustering framework that can be used to discover multiple semantic and visual senses of a given Noun Phrase (NP).
no code implementations • 11 Nov 2014 • Jiwei Li, Alan Ritter, Dan Jurafsky
by building a probabilistic model that reasons over user attributes (the user's location or gender) and the social network (the user's friends and spouse), via inferences like homophily (I am more likely to like sushi if spouse or friends like sushi, I am more likely to like the Knicks if I live in New York).
1 code implementation • TACL 2014 • Wei Xu, Alan Ritter, Chris Callison-Burch, William B. Dolan, Yangfeng Ji
We present MultiP (Multi-instance Learning Paraphrase Model), a new model suited to identify paraphrases within the short messages on Twitter.
no code implementations • TACL 2013 • Alan Ritter, Luke Zettlemoyer, {Mausam}, Oren Etzioni
Distant supervision algorithms learn information extraction models given only large readily available databases and text collections.
1 code implementation • Conference on Empirical Methods in Natural Language Processing 2011 • Alan Ritter, Sam Clark, Mausam Etzioni, Oren Etzioni
The performance of standard NLP tools is severely degraded on tweets.