33 citations · 142 across the 22 of their papers we have counts for
22 papers
Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production
Ante Wang, Linfeng Song, Qi Liu +5
Knowledge-aided dialogue response generation aims at augmenting chatbots with relevant external knowledge in the hope of generating more informative responses. The majority of prev…
Friend-training: Learning from Models of Different but Related Tasks
Mian Zhang, Lifeng Jin, Linfeng Song +3
Current self-training methods such as standard self-training, co-training, tri-training, and others often focus on improving model performance on a single task, utilizing differenc…
Learning a Grammar Inducer from Massive Uncurated Instructional Videos
Songyang Zhang, Linfeng Song, Lifeng Jin +4
Video-aided grammar induction aims to leverage video information for finding more accurate syntactic grammars for accompanying text. While previous work focuses on building systems…
Hierarchical Context Tagging for Utterance Rewriting
Lisa Jin, Linfeng Song, Lifeng Jin +2
Utterance rewriting aims to recover coreferences and omitted information from the latest turn of a multi-turn dialogue. Recently, methods that tag rather than linearly generate seq…
Distant finetuning with discourse relations for stance classification
Lifeng Jin, Kun Xu, Linfeng Song +1
Approaches for the stance classification task, an important task for understanding argumentation in debates and detecting fake news, have been relying on models which deal with ind…
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories
Wenlin Yao, Xiaoman Pan, Lifeng Jin +3
Word Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictio…
Exophoric Pronoun Resolution in Dialogues with Topic Regularization
Xintong Yu, Hongming Zhang, Yangqiu Song +3
Resolving pronouns to their referents has long been studied as a fundamental natural language understanding problem. Previous works on pronoun coreference resolution (PCR) mostly f…
Video-aided Unsupervised Grammar Induction
Songyang Zhang, Linfeng Song, Lifeng Jin +3
We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video. Existing methods of multi-modal grammar induc…
Conversational Semantic Role Labeling
Kun Xu, Han Wu, Linfeng Song +3
Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence. Traditional SRL can fail to analyze dialogues because it only works on every sin…
NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation
Xiaoyang Wang, Chen Li, Jianqiao Zhao +1
In this paper, we propose a Chinese multi-turn topic-driven conversation dataset, NaturalConv, which allows the participants to chat anything they want as long as any element from…
Structural Information Preserving for Graph-to-Text Generation
Linfeng Song, Ante Wang, Jinsong Su +4
The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or…
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data
Dian Yu, Kai Sun, Dong Yu +1
In spite of much recent research in the area, it is still unclear whether subject-area question-answering data is useful for machine reading comprehension (MRC) tasks. In this pape…
TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis
Haisong Zhang, Lemao Liu, Haiyun Jiang +14
This technique report introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. Com…
Robust Dialogue Utterance Rewriting as Sequence Tagging
Jie Hao, Linfeng Song, Liwei Wang +3
The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this t…
Automatic Summarization of Open-Domain Podcast Episodes
Kaiqiang Song, Chen Li, Xiaoyang Wang +2
We present implementation details of our abstractive summarizers that achieve competitive results on the Podcast Summarization task of TREC 2020. A concise textual summary that cap…
Better Highlighting: Creating Sub-Sentence Summary Highlights
Sangwoo Cho, Kaiqiang Song, Chen Li +3
Amongst the best means to summarize is highlighting. In this paper, we aim to generate summary highlights to be overlaid on the original documents to make it easier for readers to…
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge
Kai Sun, Dian Yu, Jianshu Chen +2
In this paper, we aim to extract commonsense knowledge to improve machine reading comprehension. We propose to represent relations implicitly by situating structured knowledge in a…
Semantic Role Labeling Guided Multi-turn Dialogue ReWriter
Kun Xu, Haochen Tan, Linfeng Song +4
For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting rid of the noises is essential to improve its perform…
Comprehensive Image Captioning via Scene Graph Decomposition
Yiwu Zhong, Liwei Wang, Jianshu Chen +2
We address the challenging problem of image captioning by revisiting the representation of image scene graph. At the core of our method lies the decomposition of a scene graph into…
Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation
Liwei Wang, Jing Huang, Yin Li +3
Weakly supervised phrase grounding aims at learning region-phrase correspondences using only image-sentence pairs. A major challenge thus lies in the missing links between image re…
A Fast and Accurate One-Stage Approach to Visual Grounding
Zhengyuan Yang, Boqing Gong, Liwei Wang +3
We propose a simple, fast, and accurate one-stage approach to visual grounding, inspired by the following insight. The performances of existing propose-and-rank two-stage methods a…
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
Kai Sun, Dian Yu, Dong Yu +1
Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chine…