16 citations · 94 across the 23 of their papers we have counts for
18 papers · 1 filter
Situated Natural Language Explanations
Zining Zhu, Haoming Jiang, Jingfeng Yang +6
Natural language is among the most accessible tools for explaining decisions to humans, and large pretrained language models (PLMs) have demonstrated impressive abilities to genera…
Graph Reasoning for Question Answering with Triplet Retrieval
Shiyang Li, Yifan Gao, Haoming Jiang +5
Answering complex questions often requires reasoning over knowledge graphs (KGs). State-of-the-art methods often utilize entities in questions to retrieve local subgraphs, which ar…
CCGen: Explainable Complementary Concept Generation in E-Commerce
Jie Huang, Yifan Gao, Zheng Li +7
We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e.g., "Digital Cameras", generating a list of complementary concepts, e.g., 1) Camera Le…
SCOTT: Self-Consistent Chain-of-Thought Distillation
Peifeng Wang, Zhengyang Wang, Zheng Li +3
Large language models (LMs) beyond a certain scale, demonstrate the emergent capability of generating free-text rationales for their predictions via chain-of-thought (CoT) promptin…
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery
Changlong Yu, Weiqi Wang, Xin Liu +6
Understanding users' intentions in e-commerce platforms requires commonsense knowledge. In this paper, we present FolkScope, an intention knowledge graph construction framework to…
Improving Task Generalization via Unified Schema Prompt
Wanjun Zhong, Yifan Gao, Ning Ding +5
Task generalization has been a long standing challenge in Natural Language Processing (NLP). Recent research attempts to improve the task generalization ability of pre-trained lang…
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training
Yifan Gao, Qingyu Yin, Zheng Li +5
Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages ha…
ProQA: Structural Prompt-based Pre-training for Unified Question Answering
Wanjun Zhong, Yifan Gao, Ning Ding +6
Question Answering (QA) is a longstanding challenge in natural language processing. Existing QA works mostly focus on specific question types, knowledge domains, or reasoning skill…
Open-Retrieval Conversational Machine Reading
Yifan Gao, Jingjing Li, Chien-Sheng Wu +2
In conversational machine reading, systems need to interpret natural language rules, answer high-level questions such as "May I qualify for VA health care benefits?", and ask follo…
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
Yifan Gao, Henghui Zhu, Patrick Ng +7
In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. Therefore, a system ne…
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading
Yifan Gao, Chien-Sheng Wu, Jingjing Li +5
Document interpretation and dialog understanding are the two major challenges for conversational machine reading. In this work, we propose Discern, a discourse-aware entailment rea…
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning
Yifan Gao, Piji Li, Wei Bi +3
Sentence function is an important linguistic feature indicating the communicative purpose in uttering a sentence. Incorporating sentence functions into conversations has shown impr…
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
Yifan Gao, Chien-Sheng Wu, Shafiq Joty +5
The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions. Existing approaches are limited…
Improving Question Generation With to the Point Context
Jingjing Li, Yifan Gao, Lidong Bing +2
Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence. A major challenge in QG is to identify answer-re…
Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling
Yifan Gao, Piji Li, Irwin King +1
We study the problem of generating interconnected questions in question-answering style conversations. Compared with previous works which generate questions based on a single sente…
Generating Distractors for Reading Comprehension Questions from Real Examinations
Yifan Gao, Lidong Bing, Piji Li +2
We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations. In contrast to all previous works, we do not aim at preparin…
Title-Guided Encoding for Keyphrase Generation
Wang Chen, Yifan Gao, Jiani Zhang +2
Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this…
Difficulty Controllable Generation of Reading Comprehension Questions
Yifan Gao, Lidong Bing, Wang Chen +2
We investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Ques…