57 citations · 178 across the 40 of their papers we have counts for
40 papers
HopPG: Self-Iterative Program Generation for Multi-Hop Question Answering over Heterogeneous Knowledge
Yingyao Wang, Yongwei Zhou, Chaoqun Duan +2
The semantic parsing-based method is an important research branch for knowledge-based question answering. It usually generates executable programs lean upon the question and then c…
Visual-Aware Text-to-Speech
Mohan Zhou, Yalong Bai, Wei Zhang +3
Dynamically synthesizing talking speech that actively responds to a listening head is critical during the face-to-face interaction. For example, the speaker could take advantage of…
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition
Tingting Ma, Qianhui Wu, Huiqiang Jiang +3
Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language. Previous…
MuGER: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering
Yingyao Wang, Junwei Bao, Chaoqun Duan +3
Hybrid question answering (HQA) aims to answer questions over heterogeneous data, including tables and passages linked to table cells. The heterogeneous data can provide different…
UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation
Yongwei Zhou, Junwei Bao, Chaoqun Duan +3
Question answering requiring discrete reasoning, e.g., arithmetic computing, comparison, and counting, over knowledge is a challenging task. In this paper, we propose UniRPG, a sem…
Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers
Wanjun Zhong, Tingting Ma, Jiahai Wang +4
This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making. Inspired by dual…
OPERA:Operation-Pivoted Discrete Reasoning over Text
Yongwei Zhou, Junwei Bao, Chaoqun Duan +7
Machine reading comprehension (MRC) that requires discrete reasoning involving symbolic operations, e.g., addition, sorting, and counting, is a challenging task. According to this…
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing
Wang Xu, Kehai Chen, Lili Mou +1
Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences. Recent studies typically represent the entire…
Decomposed Meta-Learning for Few-Shot Named Entity Recognition
Tingting Ma, Huiqiang Jiang, Qianhui Wu +2
Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples. In this paper, we present a decomposed meta-lear…
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection
Zhen Li, Bing Xu, Conghui Zhu +1
Compared with unimodal data, multimodal data can provide more features to help the model analyze the sentiment of data. Previous research works rarely consider token-level feature…
Responsive Listening Head Generation: A Benchmark Dataset and Baseline
Mohan Zhou, Yalong Bai, Wei Zhang +3
We present a new listening head generation benchmark, for synthesizing responsive feedbacks of a listener (e.g., nod, smile) during a face-to-face conversation. As the indispensabl…
A Neural Conversation Generation Model via Equivalent Shared Memory Investigation
Changzhen Ji, Yating Zhang, Xiaozhong Liu +4
Conversation generation as a challenging task in Natural Language Generation (NLG) has been increasingly attracting attention over the last years. A number of recent works adopted…
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension
Yongwei Zhou, Junwei Bao, Haipeng Sun +5
Reasoning machine reading comprehension (R-MRC) aims to answer complex questions that require discrete reasoning based on text. To support discrete reasoning, evidence, typically t…
Discriminative Reasoning for Document-level Relation Extraction
Wang Xu, Kehai Chen, Tiejun Zhao
Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i.e., pattern recognition, logical reasoning, coreference re…
Word Embedding Transformation for Robust Unsupervised Bilingual Lexicon Induction
Hailong Cao, Tiejun Zhao
Great progress has been made in unsupervised bilingual lexicon induction (UBLI) by aligning the source and target word embeddings independently trained on monolingual corpora. The…
Gumbel-Attention for Multi-modal Machine Translation
Pengbo Liu, Hailong Cao, Tiejun Zhao
Multi-modal machine translation (MMT) improves translation quality by introducing visual information. However, the existing MMT model ignores the problem that the image will bring…
Document-Level Relation Extraction with Reconstruction
Wang Xu, Kehai Chen, Tiejun Zhao
In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each…
AI-lead Court Debate Case Investigation
Changzhen Ji, Xin Zhou, Conghui Zhu +1
The multi-role judicial debate composed of the plaintiff, defendant, and judge is an important part of the judicial trial. Different from other types of dialogue, questions are rai…
Cross Copy Network for Dialogue Generation
Changzhen Ji, Xin Zhou, Yating Zhang +4
In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e.g., LSTM+attention, Pointer Generator Networks, and Transformer) t…
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
Yingyao Wang, Junwei Bao, Guangyi Liu +4
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations. Due to the fact that some relations usually keep…
Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark
Guanhua Zhang, Bing Bai, Jian Liang +3
Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts. Models utilizing these superficial cl…
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation
Haipeng Sun, Rui Wang, Kehai Chen +3
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs. However, it can only translate between a single language pair an…
Understanding Learning Dynamics for Neural Machine Translation
Conghui Zhu, Guanlin Li, Lemao Liu +2
Despite the great success of NMT, there still remains a severe challenge: it is hard to interpret the internal dynamics during its training process. In this paper we propose to und…
Detecting and Understanding Generalization Barriers for Neural Machine Translation
Guanlin Li, Lemao Liu, Conghui Zhu +2
Generalization to unseen instances is our eternal pursuit for all data-driven models. However, for realistic task like machine translation, the traditional approach measuring gener…