Search Results for author: Yongbin Li

Found 59 papers, 26 papers with code

Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark

1 code implementation26 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.

PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts

no code implementations24 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.

Iterative Forward Tuning Boosts In-context Learning in Language Models

no code implementations22 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.

Decision Making Multiple-choice

Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment

1 code implementation19 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.

Emotion Recognition in Conversation Multimodal Intent Recognition +1

Causal Document-Grounded Dialogue Pre-training

no code implementations18 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.

Long-Tailed Question Answering in an Open World

1 code implementation11 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.

Knowledge Distillation Language Modelling +1

Domain Incremental Lifelong Learning in an Open World

1 code implementation11 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.

Language Modelling

A Survey on Out-of-Distribution Detection in NLP

no code implementations5 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

Out-of-Domain Intent Detection Considering Multi-turn Dialogue Contexts

no code implementations5 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.

Intent Detection

Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs

1 code implementation4 May 2023 Jinyang Li, Binyuan Hui, Ge Qu, Binhua Li, Jiaxi Yang, 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.

Semantic Parsing SQL Parsing +1

API-Bank: A Benchmark for Tool-Augmented LLMs

no code implementations14 Apr 2023 Minghao Li, Feifan Song, Bowen Yu, Haiyang Yu, Zhoujun Li, Fei Huang, Yongbin Li

API-Bank includes 53 commonly used API tools, a complete Tool-Augmented LLM workflow, and 264 annotated dialogues that encompass a total of 568 API calls.

Language Modelling

Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs

no code implementations23 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).

Answer Generation Retrieval

Empathetic Response Generation via Emotion Cause Transition Graph

no code implementations23 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).

Empathetic Response Generation Response Generation

Plan-then-Seam: Towards Efficient Table-to-Text Generation

1 code implementation10 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.

Table-to-Text Generation

Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

1 code implementation31 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.

Semantic Parsing Table-based Fact Verification

Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing

1 code implementation18 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.

Domain Generalization Inductive Bias +3

SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph

1 code implementation5 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.

Question Answering

Towards Generalized Open Information Extraction

no code implementations29 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.

Open Information Extraction

Semi-Supervised Lifelong Language Learning

1 code implementation23 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.

Transfer Learning

UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition

1 code implementation21 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-MOSEI (using extra training data)

Contrastive Learning Emotion Recognition in Conversation +1

CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation

no code implementations21 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.

Goal-Oriented Dialog Retrieval

Towards Generalizable and Robust Text-to-SQL Parsing

1 code implementation23 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.

SQL Parsing Text-To-SQL

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing

1 code implementation21 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.

Contrastive Learning SQL Parsing +1

Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots

no code implementations20 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.

dialog state tracking Response Generation

SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation

1 code implementation14 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.

Contrastive Learning dialog state tracking +1

SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers

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.

SQL Parsing Text-To-SQL

EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models

no code implementations6 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.

A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions

no code implementations29 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.

SQL Parsing Text-To-SQL

A Frequency-aware Software Cache for Large Recommendation System Embeddings

1 code implementation8 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.

Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration

no code implementations14 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.

Language Modelling

Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing

2 code implementations28 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.

SQL Parsing Text-To-SQL

Multi-View Active Fine-Grained Recognition

1 code implementation2 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.

Fine-Grained Image Classification

Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems

no code implementations30 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.

Data Augmentation Spoken Dialogue Systems

A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots

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


S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers

no code implementations14 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.

Semantic Parsing Text-To-SQL

Linking-Enhanced Pre-Training for Table Semantic Parsing

no code implementations18 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.

Inductive Bias Language Modelling +2

Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking

no code implementations1 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)

dialog state tracking Multi-domain Dialogue State Tracking

Sequence Parallelism: Long Sequence Training from System Perspective

no code implementations26 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.

Improving Text-to-SQL with Schema Dependency Learning

no code implementations7 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.


Lyb3b at SemEval-2018 Task 12: Ensemble-based Deep Learning Models for Argument Reasoning Comprehension Task

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).

YNUDLG at IJCNLP-2017 Task 5: A CNN-LSTM Model with Attention for Multi-choice Question Answering in Examinations

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.

Question Answering

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