Search Results for author: Eunsol Choi

Found 39 papers, 21 papers with code

Can NLI Models Verify QA Systems’ Predictions?

1 code implementation Findings (EMNLP) 2021 Jifan Chen, Eunsol Choi, Greg Durrett

To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just “good enough” in the context of imperfect QA datasets.

Natural Language Inference Question Answering

Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines

1 code implementation ACL 2022 Saadia Gabriel, Skyler Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi

Even to a simple and short news headline, readers react in a multitude of ways: cognitively (e. g. inferring the writer’s intent), emotionally (e. g. feeling distrust), and behaviorally (e. g. sharing the news with their friends).

Misinformation

Modeling Exemplification in Long-form Question Answering via Retrieval

no code implementations19 May 2022 Shufan Wang, Fangyuan Xu, Laure Thompson, Eunsol Choi, Mohit Iyyer

We show that not only do state-of-the-art LFQA models struggle to generate relevant examples, but also that standard evaluation metrics such as ROUGE are insufficient to judge exemplification quality.

Question Answering

Generating Literal and Implied Subquestions to Fact-check Complex Claims

no code implementations14 May 2022 Jifan Chen, Aniruddh Sriram, Eunsol Choi, Greg Durrett

Verifying complex political claims is a challenging task, especially when politicians use various tactics to subtly misrepresent the facts.

Fact Checking

Entity Cloze By Date: What LMs Know About Unseen Entities

no code implementations5 May 2022 Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi, Greg Durrett

Given its wide coverage on entity knowledge and temporal indexing, our dataset can be used to evaluate LMs and techniques designed to modify or extend their knowledge.

How Do We Answer Complex Questions: Discourse Structure of Long-form Answers

1 code implementation ACL 2022 Fangyuan Xu, Junyi Jessy Li, Eunsol Choi

Long-form answers, consisting of multiple sentences, can provide nuanced and comprehensive answers to a broader set of questions.

Simulating Bandit Learning from User Feedback for Extractive Question Answering

1 code implementation ACL 2022 Ge Gao, Eunsol Choi, Yoav Artzi

We study learning from user feedback for extractive question answering by simulating feedback using supervised data.

Question Answering

SituatedQA: Incorporating Extra-Linguistic Contexts into QA

1 code implementation EMNLP 2021 Michael J. Q. Zhang, Eunsol Choi

To construct SituatedQA, we first identify such questions in existing QA datasets.

Learning with Different Amounts of Annotation: From Zero to Many Labels

1 code implementation EMNLP 2021 Shujian Zhang, Chengyue Gong, Eunsol Choi

Introducing such multi label examples at the cost of annotating fewer examples brings clear gains on natural language inference task and entity typing task, even when we simply first train with a single label data and then fine tune with multi label examples.

Data Augmentation Entity Typing +1

CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge

1 code implementation3 Sep 2021 Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi, Greg Durrett

We introduce CREAK, a testbed for commonsense reasoning about entity knowledge, bridging fact-checking about entities (Harry Potter is a wizard and is skilled at riding a broomstick) with commonsense inferences (if you're good at a skill you can teach others how to do it).

Fact Checking Fact Verification +1

Can NLI Models Verify QA Systems' Predictions?

1 code implementation18 Apr 2021 Jifan Chen, Eunsol Choi, Greg Durrett

To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just "good enough" in the context of imperfect QA datasets.

Natural Language Inference Question Answering

Capturing Label Distribution: A Case Study in NLI

no code implementations13 Feb 2021 Shujian Zhang, Chengyue Gong, Eunsol Choi

We depart from the standard practice of collecting a single reference per each training example, and find that collecting multiple references can achieve better accuracy under the fixed annotation budget.

Natural Language Inference

Decontextualization: Making Sentences Stand-Alone

no code implementations9 Feb 2021 Eunsol Choi, Jennimaria Palomaki, Matthew Lamm, Tom Kwiatkowski, Dipanjan Das, Michael Collins

Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context.

Question Answering

Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval

no code implementations ACL 2021 Akari Asai, Eunsol Choi

However, datasets containing information-seeking queries where evidence documents are provided after the queries are written independently remain challenging.

Language Modelling Pretrained Language Models +2

QED: A Framework and Dataset for Explanations in Question Answering

1 code implementation8 Sep 2020 Matthew Lamm, Jennimaria Palomaki, Chris Alberti, Daniel Andor, Eunsol Choi, Livio Baldini Soares, Michael Collins

A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility and trust.

Explanation Generation Question Answering

Entities as Experts: Sparse Memory Access with Entity Supervision

1 code implementation EMNLP 2020 Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, Eunsol Choi, Tom Kwiatkowski

We introduce a new model - Entities as Experts (EAE) - that can access distinct memories of the entities mentioned in a piece of text.

Language Modelling

MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension

1 code implementation WS 2019 Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, Danqi Chen

We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems.

Multi-Task Learning Question Answering +1

No Permanent Friends or Enemies: Tracking Relationships between Nations from News

1 code implementation NAACL 2019 Xiaochuang Han, Eunsol Choi, Chenhao Tan

Understanding the dynamics of international politics is important yet challenging for civilians.

pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference

3 code implementations NAACL 2019 Mandar Joshi, Eunsol Choi, Omer Levy, Daniel S. Weld, Luke Zettlemoyer

Reasoning about implied relationships (e. g., paraphrastic, common sense, encyclopedic) between pairs of words is crucial for many cross-sentence inference problems.

Common Sense Reasoning Word Embeddings

FlowQA: Grasping Flow in History for Conversational Machine Comprehension

1 code implementation ICLR 2019 Hsin-Yuan Huang, Eunsol Choi, Wen-tau Yih

Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context, and the current question.

Question Answering Reading Comprehension

QuAC: Question Answering in Context

no code implementations EMNLP 2018 Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total).

Question Answering Reading Comprehension

Neural Metaphor Detection in Context

1 code implementation EMNLP 2018 Ge Gao, Eunsol Choi, Yejin Choi, Luke Zettlemoyer

We present end-to-end neural models for detecting metaphorical word use in context.

QuAC : Question Answering in Context

no code implementations21 Aug 2018 Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total).

Question Answering Reading Comprehension

Ultra-Fine Entity Typing

no code implementations ACL 2018 Eunsol Choi, Omer Levy, Yejin Choi, Luke Zettlemoyer

We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e. g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity.

Entity Linking Entity Typing

Coarse-to-Fine Question Answering for Long Documents

no code implementations ACL 2017 Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alex Lacoste, re, Jonathan Berant

We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.

Question Answering Reading Comprehension +1

Zero-Shot Relation Extraction via Reading Comprehension

2 code implementations CONLL 2017 Omer Levy, Minjoon Seo, Eunsol Choi, Luke Zettlemoyer

We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot.

Reading Comprehension Relation Extraction +2

Hierarchical Question Answering for Long Documents

no code implementations6 Nov 2016 Eunsol Choi, Daniel Hewlett, Alexandre Lacoste, Illia Polosukhin, Jakob Uszkoreit, Jonathan Berant

We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.

Question Answering Reading Comprehension +1

Cannot find the paper you are looking for? You can Submit a new open access paper.