Eunsol Choi

Extracting Structured Scholarly Information from the Machine Translation Literature

2016

Understanding the experimental results of a scientific paper is crucial to understanding its contribution and to comparing it with related work.We introduce a structured, queryable representation for experimental results and a baseline system that automatically …

When to Use Efficient Self Attention? Profiling Text, Speech and Image Transformer Variants

2023 · arXiv (Cornell University)

We present the first unified study of the efficiency of self-attention-based Transformer variants spanning text, speech and vision. We identify input length thresholds (tipping points) at which efficient Transformer variants become more efficient than vanilla …

Modeling Exemplification in Long-form Question Answering via Retrieval

2022 · arXiv (Cornell University)

Exemplification is a process by which writers explain or clarify a concept by providing an example. While common in all forms of writing, exemplification is particularly useful in the task of long-form question answering (LFQA), …

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

2017

We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K questionanswer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high …

Coarse-to-Fine Question Answering for Long Documents

2017

Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, Jonathan Berant. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.

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 …

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 …

Ultra-Fine Entity Typing

2018

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 …

T<scp>y</scp>D<scp>i</scp> QA: A Benchmark for Information-Seeking Question Answering in <i>Ty</i>pologically <i>Di</i>verse Languages

2020 · Transactions of the Association for Computational Linguistics

Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA—a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard …