Search Results for author: Sewon Min

Found 27 papers, 19 papers with code

Zero- and Few-Shot NLP with Pretrained Language Models

no code implementations ACL 2022 Iz Beltagy, Arman Cohan, Robert Logan IV, Sewon Min, Sameer Singh

The ability to efficiently learn from little-to-no data is critical to applying NLP to tasks where data collection is costly or otherwise difficult.

Few-Shot Learning Pretrained Language Models

INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions

1 code implementation2 Jul 2022 Zeqiu Wu, Ryu Parish, Hao Cheng, Sewon Min, Prithviraj Ammanabrolu, Mari Ostendorf, Hannaneh Hajishirzi

In an information-seeking conversation, a user converses with an agent to ask a series of questions that can often be under- or over-specified.

Open-Domain Question Answering Response Generation

Revisiting Calibration for Question Answering

no code implementations25 May 2022 Chenglei Si, Chen Zhao, Sewon Min, Jordan Boyd-Graber

Model calibration aims to adjust (calibrate) models' confidence so that they match expected accuracy.

Question Answering

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

1 code implementation25 Feb 2022 Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer

Large language models (LMs) are able to in-context learn -- perform a new task via inference alone by conditioning on a few input-label pairs (demonstrations) and making predictions for new inputs.

MetaICL: Learning to Learn In Context

1 code implementation NAACL 2022 Sewon Min, Mike Lewis, Luke Zettlemoyer, Hannaneh Hajishirzi

We introduce MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learning on a large set of training tasks.

Few-Shot Learning Language Modelling +3

FaVIQ: FAct Verification from Information-seeking Questions

1 code implementation ACL 2022 Jungsoo Park, Sewon Min, Jaewoo Kang, Luke Zettlemoyer, Hannaneh Hajishirzi

Claims in FAVIQ are verified to be natural, contain little lexical bias, and require a complete understanding of the evidence for verification.

Fact Checking Fact Verification +1

RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering

no code implementations NAACL 2021 Srinivasan Iyer, Sewon Min, Yashar Mehdad, Wen-tau Yih

State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples.

Machine Reading Comprehension Natural Questions +3

Beyond Paragraphs: NLP for Long Sequences

1 code implementation NAACL 2021 Iz Beltagy, Arman Cohan, Hannaneh Hajishirzi, Sewon Min, Matthew E. Peters

In this tutorial, we aim at bringing interested NLP researchers up to speed about the recent and ongoing techniques for document-level representation learning.

Representation Learning

Joint Passage Ranking for Diverse Multi-Answer Retrieval

no code implementations EMNLP 2021 Sewon Min, Kenton Lee, Ming-Wei Chang, Kristina Toutanova, Hannaneh Hajishirzi

We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a given question.

Answer Generation Passage Ranking +2

RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering

1 code implementation21 Oct 2020 Srinivasan Iyer, Sewon Min, Yashar Mehdad, Wen-tau Yih

State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples.

Machine Reading Comprehension Natural Questions +3

Efficient One-Pass End-to-End Entity Linking for Questions

3 code implementations EMNLP 2020 Belinda Z. Li, Sewon Min, Srinivasan Iyer, Yashar Mehdad, Wen-tau Yih

We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass.

Entity Linking Question Answering

UnifiedQA: Crossing Format Boundaries With a Single QA System

2 code implementations Findings of the Association for Computational Linguistics 2020 Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, Hannaneh Hajishirzi

As evidence, we use the latest advances in language modeling to build a single pre-trained QA model, UnifiedQA, that performs surprisingly well across 17 QA datasets spanning 4 diverse formats.

Common Sense Reasoning Language Modelling +3

AmbigQA: Answering Ambiguous Open-domain Questions

1 code implementation EMNLP 2020 Sewon Min, Julian Michael, Hannaneh Hajishirzi, Luke Zettlemoyer

Ambiguity is inherent to open-domain question answering; especially when exploring new topics, it can be difficult to ask questions that have a single, unambiguous answer.

Open-Domain Question Answering

Dense Passage Retrieval for Open-Domain Question Answering

14 code implementations EMNLP 2020 Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih

Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method.

Open-Domain Question Answering Passage Retrieval

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

7 code implementations10 Nov 2019 Sewon Min, Danqi Chen, Luke Zettlemoyer, Hannaneh Hajishirzi

We introduce an approach for open-domain question answering (QA) that retrieves and reads a passage graph, where vertices are passages of text and edges represent relationships that are derived from an external knowledge base or co-occurrence in the same article.

Natural Questions Open-Domain Question Answering +3

On Making Reading Comprehension More Comprehensive

no code implementations WS 2019 Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, Sewon Min

In this work, we justify a question answering approach to reading comprehension and describe the various kinds of questions one might use to more fully test a system{'}s comprehension of a passage, moving beyond questions that only probe local predicate-argument structures.

Machine Reading Comprehension Question Answering

Question Answering is a Format; When is it Useful?

no code implementations25 Sep 2019 Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, Sewon Min

In this opinion piece, we argue that question answering should be considered a format which is sometimes useful for studying particular phenomena, not a phenomenon or task in itself.

Machine Translation Question Answering +4

Neural Speed Reading via Skim-RNN

1 code implementation ICLR 2018 Minjoon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi

Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens.

Question Answering through Transfer Learning from Large Fine-grained Supervision Data

1 code implementation ACL 2017 Sewon Min, Minjoon Seo, Hannaneh Hajishirzi

We show that the task of question answering (QA) can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset.

Question Answering Transfer Learning

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