Danqi Chen

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

2019 · arXiv (Cornell University)

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 …

A Frustratingly Easy Approach for Entity and Relation Extraction

2021

End-to-end relation extraction aims to identify named entities and extract relations between them. Most recent work models these two subtasks jointly, either by casting them in one structured prediction framework, or performing multi-task learning through …

Single-dataset Experts for Multi-dataset Question Answering

2021 · arXiv (Cornell University)

Many datasets have been created for training reading comprehension models, and a natural question is whether we can combine them to build models that (1) perform better on all of the training datasets and (2) …

Observed versus latent features for knowledge base and text inference

2015

In this paper we show the surprising effectiveness of a simple observed features model in comparison to latent feature models on two benchmark knowledge base completion datasets, FB15K and WN18. We also compare latent and …

Representing Text for Joint Embedding of Text and Knowledge Bases

2015

Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base …

Reading Wikipedia to Answer Open-Domain Questions

2017 · arXiv (Cornell University)

This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article. This task of machine reading …

Position-aware Attention and Supervised Data Improve Slot Filling

2017

Organized relational knowledge in the form of "knowledge graphs" is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses …

SpanBERT: Improving Pre-training by Representing and Predicting Spans

2020 · Transactions of the Association for Computational Linguistics

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the …

A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

2016

Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP. A key factor impeding its solution by machine learned systems is the limited …

HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case

2019 · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

Given a combinatorial optimisation problem, there are typically multiple ways of modelling it for presentation to an automated solver. Choosing the right combination of model and target solver can have a significant impact on the …

A Discrete Hard EM Approach for Weakly Supervised Question Answering

2019

Sewon Min, Danqi Chen, Hannaneh Hajishirzi, Luke Zettlemoyer. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

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.

SimCSE: Simple Contrastive Learning of Sentence Embeddings

2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

This paper presents SimCSE, a simple contrastive learning framework that greatly advances the state-of-the-art sentence embeddings. We first describe an unsupervised approach, which takes an input sentence and predicts itself in a contrastive objective, with …

Factual Probing Is [MASK]: Learning vs. Learning to Recall

2021

demonstrated that it is possible to retrieve world facts from a pretrained language model by expressing them as cloze-style prompts and interpret the model's prediction accuracy as a lower bound on the amount of factual …

Making Pre-trained Language Models Better Few-shot Learners

2021

Tianyu Gao, Adam Fisch, Danqi Chen. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.