Jure Leskovec


2022

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LinkBERT: Pretraining Language Models with Document Links
Michihiro Yasunaga | Jure Leskovec | Percy Liang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Language model (LM) pretraining captures various knowledge from text corpora, helping downstream tasks. However, existing methods such as BERT model a single document, and do not capture dependencies or knowledge that span across documents. In this work, we propose LinkBERT, an LM pretraining method that leverages links between documents, e.g., hyperlinks. Given a text corpus, we view it as a graph of documents and create LM inputs by placing linked documents in the same context. We then pretrain the LM with two joint self-supervised objectives: masked language modeling and our new proposal, document relation prediction. We show that LinkBERT outperforms BERT on various downstream tasks across two domains: the general domain (pretrained on Wikipedia with hyperlinks) and biomedical domain (pretrained on PubMed with citation links). LinkBERT is especially effective for multi-hop reasoning and few-shot QA (+5% absolute improvement on HotpotQA and TriviaQA), and our biomedical LinkBERT sets new states of the art on various BioNLP tasks (+7% on BioASQ and USMLE). We release our pretrained models, LinkBERT and BioLinkBERT, as well as code and data.

2021

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LM-Critic: Language Models for Unsupervised Grammatical Error Correction
Michihiro Yasunaga | Jure Leskovec | Percy Liang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs for training, but obtaining such annotation can be prohibitively expensive. Recently, the Break-It-Fix-It (BIFI) framework has demonstrated strong results on learning to repair a broken program without any labeled examples, but this relies on a perfect critic (e.g., a compiler) that returns whether an example is valid or not, which does not exist for the GEC task. In this work, we show how to leverage a pretrained language model (LM) in defining an LM-Critic, which judges a sentence to be grammatical if the LM assigns it a higher probability than its local perturbations. We apply this LM-Critic and BIFI along with a large set of unlabeled sentences to bootstrap realistic ungrammatical / grammatical pairs for training a corrector. We evaluate our approach on GEC datasets on multiple domains (CoNLL-2014, BEA-2019, GMEG-wiki and GMEG-yahoo) and show that it outperforms existing methods in both the unsupervised setting (+7.7 F0.5) and the supervised setting (+0.5 F0.5).

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QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
Michihiro Yasunaga | Hongyu Ren | Antoine Bosselut | Percy Liang | Jure Leskovec
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG. Here we propose a new model, QA-GNN, which addresses the above challenges through two key innovations: (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph-based message passing. We evaluate QA-GNN on the CommonsenseQA and OpenBookQA datasets, and show its improvement over existing LM and LM+KG models, as well as its capability to perform interpretable and structured reasoning, e.g., correctly handling negation in questions.

2016

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Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
William L. Hamilton | Jure Leskovec | Dan Jurafsky
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health
Tim Althoff | Kevin Clark | Jure Leskovec
Transactions of the Association for Computational Linguistics, Volume 4

Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message-based counseling conversations. We develop a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes. Applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses, we discover actionable conversation strategies that are associated with better conversation outcomes.

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Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora
William L. Hamilton | Kevin Clark | Jure Leskovec | Dan Jurafsky
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change
William L. Hamilton | Jure Leskovec | Dan Jurafsky
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Learning Linguistic Descriptors of User Roles in Online Communities
Alex Wang | William L. Hamilton | Jure Leskovec
Proceedings of the First Workshop on NLP and Computational Social Science

2014

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Exploiting Social Network Structure for Person-to-Person Sentiment Analysis
Robert West | Hristo S. Paskov | Jure Leskovec | Christopher Potts
Transactions of the Association for Computational Linguistics, Volume 2

Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A’s opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.

2013

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A computational approach to politeness with application to social factors
Cristian Danescu-Niculescu-Mizil | Moritz Sudhof | Dan Jurafsky | Jure Leskovec | Christopher Potts
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)