Wen-tau Yih

Joint Verification and Reranking for Open Fact Checking Over Tables

2021

Michael Sejr Schlichtkrull, Vladimir Karpukhin, Barlas Oguz, Mike Lewis, Wen-tau Yih, Sebastian Riedel. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing …

One Embedder, Any Task: Instruction-Finetuned Text Embeddings

2023

Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, Tao Yu. Findings of the Association for Computational Linguistics: ACL 2023. 2023.

Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base

2015

Wen-tau Yih, Ming-Wei Chang, Xiaodong He, Jianfeng Gao. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

WikiQA: A Challenge Dataset for Open-Domain Question Answering

2015

We describe the WIKIQA dataset, a new publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering. Most previous work on answer sentence selection focuses on a dataset …

Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text

2016

Modeling relation paths has offered significant gains in embedding models for knowledge base (KB) completion. However, enumerating paths between two entities is very expensive, and existing approaches typically resort to approximation with a sampled subset. …

The Value of Semantic Parse Labeling for Knowledge Base Question Answering

2016

We demonstrate the value of collecting semantic parse labels for knowledge base question answering. In particular, (1) unlike previous studies on small-scale datasets, we show that learning from labeled semantic parses significantly improves overall performance, …

A Knowledge-Grounded Neural Conversation Model

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Neural network models are capable of generating extremely natural sounding conversational interactions. However, these models have been mostly applied to casual scenarios (e.g., as “chatbots”) and have yet to demonstrate they can serve in more …

Search-based Neural Structured Learning for Sequential Question Answering

2017

Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore …

Cross-Sentence N-ary Relation Extraction with Graph LSTMs

2017 · arXiv (Cornell University)

Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. …

QuAC: Question Answering in Context

2018

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform …

Dissecting Contextual Word Embeddings: Architecture and Representation

2018

Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks. However, many questions remain …

FlowQA: Grasping Flow in History for Conversational Machine\n Comprehension

2018 · arXiv (Cornell University)

Conversational machine comprehension requires the understanding of the\nconversation history, such as previous question/answer pairs, the document\ncontext, and the current question. To enable traditional, single-turn models to\nencode the history comprehensively, we introduce Flow, a mechanism that …

Cross-Sentence <i>N</i>-ary Relation Extraction with Graph LSTMs

2017 · Transactions of the Association for Computational Linguistics

Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. …

QUAREL: A Dataset and Models for Answering Questions about Qualitative Relationships

2019

Many natural la guage questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods. Qualitative modeling provides tools that support such reasoning, but …

Dense Passage Retrieval for Open-Domain Question Answering

2020

Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.

Affordance-Compiled Intelligence: Observable-Only Cognitive Impedance Matching for No-Meta LLM-Integrated Systems

2026 · arXiv (Cornell University)

Affordance-Compiled Intelligence develops Cognitive Impedance Matching Theory (CIMT), an observable-only and no-meta protected compiler theory for LLM-integrated systems. The paper studies how a fixed model-policy can exhibit different operational capability when the surrounding world is …

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

2023

Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, Hannaneh Hajishirzi. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.