no code implementations • Findings (ACL) 2022 • Binyuan Hui, Ruiying Geng, Lihan Wang, Bowen Qin, Yanyang Li, Bowen Li, Jian Sun, Yongbin Li
The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing.
1 code implementation • 22 Apr 2024 • Zhengwei Tao, Ting-En Lin, Xiancai Chen, Hangyu Li, Yuchuan Wu, Yongbin Li, Zhi Jin, Fei Huang, DaCheng Tao, Jingren Zhou
Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications.
no code implementations • 29 Mar 2024 • Qinhao Zhou, Zihan Zhang, Xiang Xiang, Ke Wang, Yuchuan Wu, Yongbin Li
As intelligent agents, LLMs need to have the capabilities of task planning, long-term memory, and the ability to leverage external tools to achieve satisfactory performance.
no code implementations • 29 Mar 2024 • Yuwen Tan, Qinhao Zhou, Xiang Xiang, Ke Wang, Yuchuan Wu, Yongbin Li
We observe that adapter tuning demonstrates superiority over prompt-based methods, even without parameter expansion in each learning session.
1 code implementation • 28 Mar 2024 • Hao Lang, Fei Huang, Yongbin Li
RLHF contains three steps, i. e., human preference collecting, reward learning, and policy optimization, which are usually performed serially.
1 code implementation • 17 Mar 2024 • Feifan Song, Bowen Yu, Hao Lang, Haiyang Yu, Fei Huang, Houfeng Wang, Yongbin Li
Additionally, the concept of diversity for prompts can be more complex than responses that are typically quantified by single digits.
1 code implementation • 4 Mar 2024 • Changyu Chen, Xiting Wang, Ting-En Lin, Ang Lv, Yuchuan Wu, Xin Gao, Ji-Rong Wen, Rui Yan, Yongbin Li
In reasoning tasks, even a minor error can cascade into inaccurate results, leading to suboptimal performance of large language models in such domains.
no code implementations • 27 Feb 2024 • Xinyu Lu, Bowen Yu, Yaojie Lu, Hongyu Lin, Haiyang Yu, Le Sun, Xianpei Han, Yongbin Li
The alignment problem in Large Language Models (LLMs) involves adapting them to the broad spectrum of human values.
no code implementations • 23 Feb 2024 • Qiaoyu Tang, Jiawei Chen, Bowen Yu, Yaojie Lu, Cheng Fu, Haiyang Yu, Hongyu Lin, Fei Huang, Ben He, Xianpei Han, Le Sun, Yongbin Li
The rise of large language models (LLMs) has transformed the role of information retrieval (IR) systems in the way to humans accessing information.
1 code implementation • 2 Jan 2024 • Shujie Li, Liang Li, Ruiying Geng, Min Yang, Binhua Li, Guanghu Yuan, Wanwei He, Shao Yuan, Can Ma, Fei Huang, Yongbin Li
In this paper, we unify different types of structured data (i. e., table, key-value data, knowledge graph) into the graph format and cast different data-to-text generation tasks as graph-to-text generation.
no code implementations • 2 Jan 2024 • Zhichao Yin, Binyuan Hui, Min Yang, Fei Huang, Yongbin Li
Recently, substantial advancements in pre-trained vision-language models have greatly enhanced the capabilities of multi-modal dialog systems.
1 code implementation • 16 Dec 2023 • Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li
Nuggets assesses the potential of individual instruction examples to act as effective one shot examples, thereby identifying those that can significantly enhance diverse task performance.
1 code implementation • 7 Dec 2023 • Yuhan Chen, Ang Lv, Ting-En Lin, Changyu Chen, Yuchuan Wu, Fei Huang, Yongbin Li, Rui Yan
Specifically, the crucial information in the context will be potentially overlooked by model when it is positioned in the trough zone of the attention waveform, leading to decreased performance.
Ranked #2 on Trajectory Planning on ToolBench
1 code implementation • 6 Nov 2023 • Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li
Then, we use DARE as a versatile plug-and-play technique to sparsify delta parameters of multiple SFT homologous models for mitigating parameter interference and merge them into a single model by parameter fusing.
no code implementations • 30 Oct 2023 • Huawen Feng, Yan Fan, Xiong Liu, Ting-En Lin, Zekun Yao, Yuchuan Wu, Fei Huang, Yongbin Li, Qianli Ma
Despite the recent progress in text summarization made by large language models (LLMs), they often generate summaries that are factually inconsistent with original articles, known as "hallucinations" in text generation.
1 code implementation • 23 Oct 2023 • Qi Gou, Zehua Xia, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li, Nguyen Cam-Tu
Given a textual passage and an answer, humans are able to ask questions with various expressions, but this ability is still challenging for most question generation (QG) systems.
1 code implementation • 23 Oct 2023 • Yuanxing Liu, Wei-Nan Zhang, Yifan Chen, Yuchi Zhang, Haopeng Bai, Fan Feng, Hengbin Cui, Yongbin Li, Wanxiang Che
This paper investigates the effectiveness of combining LLM and CRS in E-commerce pre-sales dialogues, proposing two collaboration methods: CRS assisting LLM and LLM assisting CRS.
1 code implementation • 20 Oct 2023 • Zehua Xia, Qi Gou, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li, Cam-Tu Nguyen
Previous studies have suggested that key phrase selection is essential for question generation (QG), yet it is still challenging to connect such disjointed phrases into meaningful questions, particularly for long context.
no code implementations • 12 Oct 2023 • Yi Dai, Hao Lang, Kaisheng Zeng, Fei Huang, Yongbin Li
Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning.
no code implementations • 10 Oct 2023 • Tianshu Yu, Ting-En Lin, Yuchuan Wu, Min Yang, Fei Huang, Yongbin Li
This limitation leads to suboptimal performance, even when ample training data is available.
no code implementations • 22 Sep 2023 • Haoyu Gao, Ting-En Lin, Hangyu Li, Min Yang, Yuchuan Wu, Wentao Ma, Yongbin Li
Task-oriented dialogue (TOD) systems facilitate users in executing various activities via multi-turn dialogues, but Large Language Models (LLMs) often struggle to comprehend these intricate contexts.
no code implementations • 20 Sep 2023 • Yucheng Cai, Wentao Ma, Yuchuan Wu, Shuzheng Si, Yuan Shao, Zhijian Ou, Yongbin Li
Using the high-quality prompts generated, we scale the corpus of the pre-trained conversation model to 122 datasets from 15 dialog-related tasks, resulting in Universal Pre-trained Conversation Model (UniPCM), a powerful foundation model for various conversational tasks and different dialog systems.
no code implementations • 14 Sep 2023 • Yunshui Li, Binyuan Hui, Zhaochao Yin, Wanwei He, Run Luo, Yuxing Long, Min Yang, Fei Huang, Yongbin Li
Visually-grounded dialog systems, which integrate multiple modes of communication such as text and visual inputs, have become an increasingly popular area of investigation.
2 code implementations • 4 Sep 2023 • Zaijing Li, Ting-En Lin, Yuchuan Wu, Meng Liu, Fengxiao Tang, Ming Zhao, Yongbin Li
Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
1 code implementation • 10 Aug 2023 • Yingxiu Zhao, Bowen Yu, Binyuan Hui, Haiyang Yu, Fei Huang, Yongbin Li, Nevin L. Zhang
Training large language models (LLMs) with open-domain instruction data has yielded remarkable success in aligning to end tasks and human preferences.
1 code implementation • 3 Aug 2023 • Xinghua Zhang, Bowen Yu, Haiyang Yu, Yangyu Lv, Tingwen Liu, Fei Huang, Hongbo Xu, Yongbin Li
Each perspective corresponds to the role of a specific LLM neuron in the first layer.
1 code implementation • 30 Jun 2023 • Feifan Song, Bowen Yu, Minghao Li, Haiyang Yu, Fei Huang, Yongbin Li, Houfeng Wang
In this manner, PRO effectively transforms human alignment into aligning the probability ranking of n responses generated by LLM with the preference ranking of humans towards these responses.
no code implementations • 29 Jun 2023 • Bowen Yu, Cheng Fu, Haiyang Yu, Fei Huang, Yongbin Li
When trying to answer complex questions, people often rely on multiple sources of information, such as visual, textual, and tabular data.
no code implementations • 20 Jun 2023 • Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li
To alleviate these limitations, in this paper, we present CATS, a pragmatic Chinese answer-to-sequence dataset with large scale and high quality.
no code implementations • 18 Jun 2023 • Xin Cong. Bowen Yu, Mengcheng Fang, Tingwen Liu, Haiyang Yu, Zhongkai Hu, Fei Huang, Yongbin Li, Bin Wang
Inspired by the fact that large amount of knowledge are stored in the pretrained language models~(PLM) and can be retrieved explicitly, in this paper, we propose MetaRetriever to retrieve task-specific knowledge from PLMs to enhance universal IE.
no code implementations • 12 Jun 2023 • Hao Sun, Yang Li, Liwei Deng, Bowen Li, Binyuan Hui, Binhua Li, Yunshi Lan, Yan Zhang, Yongbin Li
Context information modeling is an important task in conversational KBQA.
1 code implementation • 26 May 2023 • Yuxing Long, Binyuan Hui, Caixia Yuan1, Fei Huang, Yongbin Li, Xiaojie Wang
Existing multimodal task-oriented dialog data fails to demonstrate the diverse expressions of user subjective preferences and recommendation acts in the real-life shopping scenario.
1 code implementation • 24 May 2023 • Yunshui Li, Binyuan Hui, Zhichao Yin, Min Yang, Fei Huang, Yongbin Li
It utilizes a combination of several fundamental experts to accommodate multiple dialogue-related tasks and can be pre-trained using limited dialogue and extensive non-dialogue multi-modal data.
Ranked #1 on Response Generation on SIMMC2.0
no code implementations • NeurIPS 2023 • Shuzheng Si, Wentao Ma, Haoyu Gao, Yuchuan Wu, Ting-En Lin, Yinpei Dai, Hangyu Li, Rui Yan, Fei Huang, Yongbin Li
SpokenWOZ further incorporates common spoken characteristics such as word-by-word processing and reasoning in spoken language.
no code implementations • 22 May 2023 • Jiaxi Yang, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, Yongbin Li
In this paper, we propose an effective and efficient two-stage framework to boost ICL in LLMs by exploiting a dual form between Transformer attention and gradient descent-based optimization.
1 code implementation • 19 May 2023 • Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li
In this paper, we propose Speech-text dialog Pre-training for spoken dialog understanding with ExpliCiT cRoss-Modal Alignment (SPECTRA), which is the first-ever speech-text dialog pre-training model.
Ranked #1 on Multimodal Sentiment Analysis on MOSI
Emotion Recognition in Conversation Multimodal Intent Recognition +1
1 code implementation • 18 May 2023 • Yingxiu Zhao, Bowen Yu, Haiyang Yu, Bowen Li, Jinyang Li, Chao Wang, Fei Huang, Yongbin Li, Nevin L. Zhang
To tackle this issue, we are the first to present a causally-complete dataset construction strategy for building million-level DocGD pre-training corpora.
1 code implementation • 11 May 2023 • Yi Dai, Hao Lang, Yinhe Zheng, Bowen Yu, Fei Huang, Yongbin Li
Specifically, we dedicate task-level prompts to capture task-specific knowledge to retain high LL performances and maintain instance-level prompts to learn knowledge shared across input samples to improve the model's generalization performance.
1 code implementation • 11 May 2023 • Yi Dai, Hao Lang, Yinhe Zheng, Fei Huang, Yongbin Li
A retrieve-then-rerank frame is further introduced to select in-context examples, which guild the LM to generate text that express knowledge for QA tasks.
no code implementations • 5 May 2023 • Yuanxing Liu, Weinan Zhang, Baohua Dong, Yan Fan, Hang Wang, Fan Feng, Yifan Chen, Ziyu Zhuang, Hengbin Cui, Yongbin Li, Wanxiang Che
In this paper, we construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios.
no code implementations • 5 May 2023 • Hao Lang, Yinhe Zheng, Yixuan Li, Jian Sun, Fei Huang, Yongbin Li
Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 5 May 2023 • Hao Lang, Yinhe Zheng, Binyuan Hui, Fei Huang, Yongbin Li
Out-of-Domain (OOD) intent detection is vital for practical dialogue systems, and it usually requires considering multi-turn dialogue contexts.
1 code implementation • 4 May 2023 • Haoyu Gao, Rui Wang, Ting-En Lin, Yuchuan Wu, Min Yang, Fei Huang, Yongbin Li
Dialogue Topic Segmentation (DTS) plays an essential role in a variety of dialogue modeling tasks.
no code implementations • NeurIPS 2023 • Jinyang Li, Binyuan Hui, Ge Qu, Jiaxi Yang, Binhua Li, Bowen Li, Bailin Wang, Bowen Qin, Rongyu Cao, Ruiying Geng, Nan Huo, Xuanhe Zhou, Chenhao Ma, Guoliang Li, Kevin C. C. Chang, Fei Huang, Reynold Cheng, Yongbin Li
Our emphasis on database values highlights the new challenges of dirty database contents, external knowledge between NL questions and database contents, and SQL efficiency, particularly in the context of massive databases.
Ranked #1 on Text-To-SQL on BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation) (Execution Accurarcy (Human) metric)
no code implementations • 14 Apr 2023 • Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, Yongbin Li
(2) How can we enhance LLMs' ability to utilize tools?
no code implementations • 23 Feb 2023 • Yushan Qian, Bo wang, Ting-En Lin, Yinhe Zheng, Ying Zhu, Dongming Zhao, Yuexian Hou, Yuchuan Wu, Yongbin Li
Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e. g., emotion status) and cognitive factors (e. g., cause of the emotion).
no code implementations • 23 Feb 2023 • Yeqin Zhang, Haomin Fu, Cheng Fu, Haiyang Yu, Yongbin Li, Cam-Tu Nguyen
Specifically, the former efficiently finds relevant passages in a retrieval-and-reranking process, whereas the latter effectively extracts finer-grain spans within those passages to incorporate into a parametric answer generation model (BART, T5).
1 code implementation • 10 Feb 2023 • Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Binhua Li, Yongbin Li
Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables.
1 code implementation • 31 Jan 2023 • Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, Yongbin Li
To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning; and (ii) decompose complex questions into simpler sub-questions for text reasoning.
Ranked #1 on Table-based Fact Verification on TabFact
1 code implementation • 18 Jan 2023 • Jinyang Li, Binyuan Hui, Reynold Cheng, Bowen Qin, Chenhao Ma, Nan Huo, Fei Huang, Wenyu Du, Luo Si, Yongbin Li
Recently, the pre-trained text-to-text transformer model, namely T5, though not specialized for text-to-SQL parsing, has achieved state-of-the-art performance on standard benchmarks targeting domain generalization.
Ranked #4 on Semantic Parsing on spider
1 code implementation • 5 Jan 2023 • Yuxing Long, Binyuan Hui, Fulong Ye, Yanyang Li, Zhuoxin Han, Caixia Yuan, Yongbin Li, Xiaojie Wang
Existing multimodal conversation agents have shown impressive abilities to locate absolute positions or retrieve attributes in simple scenarios, but they fail to perform well when complex relative positions and information alignments are involved, which poses a bottleneck in response quality.
no code implementations • 29 Nov 2022 • Bowen Yu, Zhenyu Zhang, Jingyang Li, Haiyang Yu, Tingwen Liu, Jian Sun, Yongbin Li, Bin Wang
Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts.
1 code implementation • 23 Nov 2022 • Yingxiu Zhao, Yinhe Zheng, Bowen Yu, Zhiliang Tian, Dongkyu Lee, Jian Sun, Haiyang Yu, Yongbin Li, Nevin L. Zhang
In this paper, we explore a novel setting, semi-supervised lifelong language learning (SSLL), where a model learns sequentially arriving language tasks with both labeled and unlabeled data.
1 code implementation • 21 Nov 2022 • Guimin Hu, Ting-En Lin, Yi Zhao, Guangming Lu, Yuchuan Wu, Yongbin Li
Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors.
Ranked #1 on Multimodal Sentiment Analysis on CMU-MOSI
no code implementations • 21 Nov 2022 • Yinpei Dai, Wanwei He, Bowen Li, Yuchuan Wu, Zheng Cao, Zhongqi An, Jian Sun, Yongbin Li
Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data.
no code implementations • 10 Nov 2022 • Hao Lang, Yinhe Zheng, Jian Sun, Fei Huang, Luo Si, Yongbin Li
Out-of-Domain (OOD) intent detection is important for practical dialog systems.
1 code implementation • 27 Oct 2022 • Che Liu, Rui Wang, Junfeng Jiang, Yongbin Li, Fei Huang
In this paper, we introduce the task of learning unsupervised dialogue embeddings.
1 code implementation • 23 Oct 2022 • Chang Gao, Bowen Li, Wenxuan Zhang, Wai Lam, Binhua Li, Fei Huang, Luo Si, Yongbin Li
Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries.
1 code implementation • 21 Oct 2022 • ZeFeng Cai, Xiangyu Li, Binyuan Hui, Min Yang, Bowen Li, Binhua Li, Zheng Cao, Weijie Li, Fei Huang, Luo Si, Yongbin Li
Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation.
no code implementations • 20 Oct 2022 • Haomin Fu, Yeqin Zhang, Haiyang Yu, Jian Sun, Fei Huang, Luo Si, Yongbin Li, Cam-Tu Nguyen
This paper introduces Doc2Bot, a novel dataset for building machines that help users seek information via conversations.
1 code implementation • 14 Oct 2022 • Yingxiu Zhao, Yinhe Zheng, Zhiliang Tian, Chang Gao, Bowen Yu, Haiyang Yu, Yongbin Li, Jian Sun, Nevin L. Zhang
Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems.
no code implementations • COLING 2022 • Liang Li, Ruiying Geng, Bowen Li, Can Ma, Yinliang Yue, Binhua Li, Yongbin Li
Most graph-to-text works are built on the encoder-decoder framework with cross-attention mechanism.
1 code implementation • COLING 2022 • Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li
To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other.
1 code implementation • COLING 2022 • Wanwei He, Yinpei Dai, Binyuan Hui, Min Yang, Zheng Cao, Jianbo Dong, Fei Huang, Luo Si, Yongbin Li
Pre-training methods with contrastive learning objectives have shown remarkable success in dialog understanding tasks.
1 code implementation • 14 Sep 2022 • Wanwei He, Yinpei Dai, Min Yang, Jian Sun, Fei Huang, Luo Si, Yongbin Li
To capture the structured dialog semantics, we pre-train the dialog understanding module via a novel tree-induced semi-supervised contrastive learning objective with the help of extra dialog annotations.
no code implementations • 6 Sep 2022 • Jiangsu Du, Ziming Liu, Jiarui Fang, Shenggui Li, Yongbin Li, Yutong Lu, Yang You
Although the AI community has expanded the model scale to the trillion parameter level, the practical deployment of 10-100 billion parameter models is still uncertain due to the latency, throughput, and memory constraints.
no code implementations • 29 Aug 2022 • Bowen Qin, Binyuan Hui, Lihan Wang, Min Yang, Jinyang Li, Binhua Li, Ruiying Geng, Rongyu Cao, Jian Sun, Luo Si, Fei Huang, Yongbin Li
In recent years, deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output SQL query.
1 code implementation • 8 Aug 2022 • Jiarui Fang, Geng Zhang, Jiatong Han, Shenggui Li, Zhengda Bian, Yongbin Li, Jin Liu, Yang You
Deep learning recommendation models (DLRMs) have been widely applied in Internet companies.
no code implementations • 14 Jul 2022 • Zhenyu Zhang, Bowen Yu, Haiyang Yu, Tingwen Liu, Cheng Fu, Jingyang Li, Chengguang Tang, Jian Sun, Yongbin Li
In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems.
2 code implementations • 28 Jun 2022 • Lihan Wang, Bowen Qin, Binyuan Hui, Bowen Li, Min Yang, Bailin Wang, Binhua Li, Fei Huang, Luo Si, Yongbin Li
The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i. e., properly recognizing mentions of unseen columns or tables when generating SQLs.
1 code implementation • 2 Jun 2022 • Ruoyi Du, Wenqing Yu, Heqing Wang, Dongliang Chang, Ting-En Lin, Yongbin Li, Zhanyu Ma
As fine-grained visual classification (FGVC) being developed for decades, great works related have exposed a key direction -- finding discriminative local regions and revealing subtle differences.
no code implementations • 30 May 2022 • Ting-En Lin, Yuchuan Wu, Fei Huang, Luo Si, Jian Sun, Yongbin Li
In this paper, we present Duplex Conversation, a multi-turn, multimodal spoken dialogue system that enables telephone-based agents to interact with customers like a human.
no code implementations • 24 May 2022 • Shaowen Zhou, Bowen Yu, Aixin Sun, Cheng Long, Jingyang Li, Haiyang Yu, Jian Sun, Yongbin Li
Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora.
Ranked #1 on Open Information Extraction on CaRB
Natural Language Understanding Open-Domain Question Answering +1
no code implementations • 21 May 2022 • Jing Ma, Xiang Xiang, Ke Wang, Yuchuan Wu, Yongbin Li
Black-Box Knowledge Distillation (B2KD) is a formulated problem for cloud-to-edge model compression with invisible data and models hosted on the server.
1 code implementation • Findings (ACL) 2022 • Sai Zhang, Yuwei Hu, Yuchuan Wu, Jiaman Wu, Yongbin Li, Jian Sun, Caixia Yuan, Xiaojie Wang
We find some new linguistic phenomena and interactive manners in SSTOD which raise critical challenges of building dialog agents for the task.
Ranked #1 on SSTOD on SSD_NAME
no code implementations • 14 Mar 2022 • Binyuan Hui, Ruiying Geng, Lihan Wang, Bowen Qin, Bowen Li, Jian Sun, Yongbin Li
The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing.
1 code implementation • 29 Nov 2021 • Wanwei He, Yinpei Dai, Yinhe Zheng, Yuchuan Wu, Zheng Cao, Dermot Liu, Peng Jiang, Min Yang, Fei Huang, Luo Si, Jian Sun, Yongbin Li
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems.
Ranked #1 on End-To-End Dialogue Modelling on MULTIWOZ 2.0
no code implementations • 18 Nov 2021 • Bowen Qin, Lihan Wang, Binyuan Hui, Ruiying Geng, Zheng Cao, Min Yang, Jian Sun, Yongbin Li
Recently pre-training models have significantly improved the performance of various NLP tasks by leveraging large-scale text corpora to improve the contextual representation ability of the neural network.
no code implementations • ACL 2021 • Yinpei Dai, Hangyu Li, Yongbin Li, Jian Sun, Fei Huang, Luo Si, Xiaodan Zhu
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.
no code implementations • 1 Jun 2021 • Yinpei Dai, Hangyu Li, Yongbin Li, Jian Sun, Fei Huang, Luo Si, Xiaodan Zhu
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.
Ranked #1 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1 (using extra training data)
no code implementations • 26 May 2021 • Shenggui Li, Fuzhao Xue, Chaitanya Baranwal, Yongbin Li, Yang You
That is, with sparse attention, our sequence parallelism enables us to train transformer with infinite long sequence.
no code implementations • 7 Mar 2021 • Binyuan Hui, Xiang Shi, Ruiying Geng, Binhua Li, Yongbin Li, Jian Sun, Xiaodan Zhu
In this paper, we present the Schema Dependency guided multi-task Text-to-SQL model (SDSQL) to guide the network to effectively capture the interactions between questions and schemas.
2 code implementations • 5 Jan 2021 • Binyuan Hui, Ruiying Geng, Qiyu Ren, Binhua Li, Yongbin Li, Jian Sun, Fei Huang, Luo Si, Pengfei Zhu, Xiaodan Zhu
Semantic parsing has long been a fundamental problem in natural language processing.
Ranked #5 on Dialogue State Tracking on CoSQL
no code implementations • ACL 2020 • Yinpei Dai, Hangyu Li, Chengguang Tang, Yongbin Li, Jian Sun, Xiaodan Zhu
Existing end-to-end dialog systems perform less effectively when data is scarce.
no code implementations • ACL 2020 • Ruiying Geng, Binhua Li, Yongbin Li, Jian Sun, Xiaodan Zhu
This paper proposes Dynamic Memory Induction Networks (DMIN) for few-shot text classification.
no code implementations • 5 May 2020 • Yinpei Dai, Huihua Yu, Yixuan Jiang, Chengguang Tang, Yongbin Li, Jian Sun
Dialog management (DM) is a crucial component in a task-oriented dialog system.
5 code implementations • IJCNLP 2019 • Ruiying Geng, Binhua Li, Yongbin Li, Xiaodan Zhu, Ping Jian, Jian Sun
Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries.
Ranked #1 on Few-Shot Text Classification on ODIC 5-way (10-shot)
no code implementations • SEMEVAL 2018 • Yongbin Li, Xiaobing Zhou
In this task, given a natural language argument with a reason and a claim, the goal is to choose the correct implicit reasoning from two options, in order to form a reasonable structure of (Reason, Warrant, Claim).
no code implementations • SEMEVAL 2018 • Yongbin Li, Xiaobing Zhou
This paper is to solve the task 11 of SemEval-2018, Machine Comprehension using Commonsense Knowledge task.
no code implementations • IJCNLP 2017 • Min Wang, Qingxun Liu, Peng Ding, Yongbin Li, Xiaobing Zhou
In this paper, we perform convolutional neural networks (CNN) to learn the joint representations of question-answer pairs first, then use the joint representations as the inputs of the long short-term memory (LSTM) with attention to learn the answer sequence of a question for labeling the matching quality of each answer.