Search Results for author: Dan Jurafsky

Found 92 papers, 39 papers with code

Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words

1 code implementation ACL 2022 Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky

Cosine similarity of contextual embeddings is used in many NLP tasks (e. g., QA, IR, MT) and metrics (e. g., BERTScore).

Richer Countries and Richer Representations

1 code implementation Findings (ACL) 2022 Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky

We examine whether some countries are more richly represented in embedding space than others.

Modular Domain Adaptation

1 code implementation Findings (ACL) 2022 Junshen K. Chen, Dallas Card, Dan Jurafsky

Off-the-shelf models are widely used by computational social science researchers to measure properties of text, such as sentiment.

Domain Adaptation Text Classification

Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference

1 code implementation EMNLP 2021 William Held, Dan Iter, Dan Jurafsky

We model the entities/events in a reader's focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters.

Coreference Resolution Entity Cross-Document Coreference Resolution +1

The Emergence of the Shape Bias Results from Communicative Efficiency

1 code implementation CoNLL (EMNLP) 2021 Eva Portelance, Michael C. Frank, Dan Jurafsky, Alessandro Sordoni, Romain Laroche

By the age of two, children tend to assume that new word categories are based on objects' shape, rather than their color or texture; this assumption is called the shape bias.

On the Opportunities and Risks of Foundation Models

no code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions

1 code implementation ACL 2021 Dorottya Demszky, Jing Liu, Zid Mancenido, Julie Cohen, Heather Hill, Dan Jurafsky, Tatsunori Hashimoto

In conversation, uptake happens when a speaker builds on the contribution of their interlocutor by, for example, acknowledging, repeating or reformulating what they have said.

Question Answering

Attention Flows are Shapley Value Explanations

no code implementations ACL 2021 Kawin Ethayarajh, Dan Jurafsky

Shapley Values, a solution to the credit assignment problem in cooperative game theory, are a popular type of explanation in machine learning, having been used to explain the importance of features, embeddings, and even neurons.

Sensitivity as a Complexity Measure for Sequence Classification Tasks

1 code implementation21 Apr 2021 Michael Hahn, Dan Jurafsky, Richard Futrell

We introduce a theoretical framework for understanding and predicting the complexity of sequence classification tasks, using a novel extension of the theory of Boolean function sensitivity.

Classification General Classification +1

Frequency-based Distortions in Contextualized Word Embeddings

no code implementations17 Apr 2021 Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky

How does word frequency in pre-training data affect the behavior of similarity metrics in contextualized BERT embeddings?

Semantic Similarity Semantic Textual Similarity +1

Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages

1 code implementation26 Mar 2021 Nay San, Martijn Bartelds, Mitchell Browne, Lily Clifford, Fiona Gibson, John Mansfield, David Nash, Jane Simpson, Myfany Turpin, Maria Vollmer, Sasha Wilmoth, Dan Jurafsky

Surprisingly, the English model outperformed the multilingual model on 4 Australian language datasets, raising questions around how to optimally leverage self-supervised speech representations for QbE-STD.

Automatic Speech Recognition

Detecting Stance in Media on Global Warming

1 code implementation Findings of the Association for Computational Linguistics 2020 Yiwei Luo, Dallas Card, Dan Jurafsky

We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.

Causal Effects of Linguistic Properties

1 code implementation NAACL 2021 Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar

Second, in practice, we only have access to noisy proxies for the linguistic properties of interest -- e. g., predictions from classifiers and lexicons.

Language Modelling

Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation

2 code implementations NAACL 2021 Yasuhide Miura, Yuhao Zhang, Emily Bao Tsai, Curtis P. Langlotz, Dan Jurafsky

We further show via a human evaluation and a qualitative analysis that our system leads to generations that are more factually complete and consistent compared to the baselines.

Natural Language Inference Text Generation

With Little Power Comes Great Responsibility

1 code implementation EMNLP 2020 Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, Dan Jurafsky

Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored by the NLP community.

Experimental Design Machine Translation +1

Nearest Neighbor Machine Translation

1 code implementation ICLR 2021 Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis

We introduce $k$-nearest-neighbor machine translation ($k$NN-MT), which predicts tokens with a nearest neighbor classifier over a large datastore of cached examples, using representations from a neural translation model for similarity search.

Machine Translation Translation

Utility is in the Eye of the User: A Critique of NLP Leaderboards

no code implementations EMNLP 2020 Kawin Ethayarajh, Dan Jurafsky

Benchmarks such as GLUE have helped drive advances in NLP by incentivizing the creation of more accurate models.

Fairness Frame

The Role of Verb Semantics in Hungarian Verb-Object Order

no code implementations16 Jun 2020 Dorottya Demszky, László Kálmán, Dan Jurafsky, Beth Levin

We test the effect of lexical semantics on the ordering of verbs and their objects by grouping verbs into 11 semantic classes.

Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models

1 code implementation ACL 2020 Dan Iter, Kelvin Guu, Larry Lansing, Dan Jurafsky

Recent models for unsupervised representation learning of text have employed a number of techniques to improve contextual word representations but have put little focus on discourse-level representations.

Common Sense Reasoning Natural Language Inference +2

A Framework for the Computational Linguistic Analysis of Dehumanization

no code implementations6 Mar 2020 Julia Mendelsohn, Yulia Tsvetkov, Dan Jurafsky

Dehumanization is a pernicious psychological process that often leads to extreme intergroup bias, hate speech, and violence aimed at targeted social groups.

Abusive Language

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

2 code implementations31 Jan 2020 Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau

Accurate reporting of energy and carbon usage is essential for understanding the potential climate impacts of machine learning research.

reinforcement-learning

Automatically Neutralizing Subjective Bias in Text

1 code implementation21 Nov 2019 Reid Pryzant, Richard Diehl Martinez, Nathan Dass, Sadao Kurohashi, Dan Jurafsky, Diyi Yang

To address this issue, we introduce a novel testbed for natural language generation: automatically bringing inappropriately subjective text into a neutral point of view ("neutralizing" biased text).

Text Generation

Social Bias Frames: Reasoning about Social and Power Implications of Language

no code implementations ACL 2020 Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, Yejin Choi

We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others.

Generalization through Memorization: Nearest Neighbor Language Models

2 code implementations ICLR 2020 Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis

Applying this augmentation to a strong Wikitext-103 LM, with neighbors drawn from the original training set, our $k$NN-LM achieves a new state-of-the-art perplexity of 15. 79 - a 2. 9 point improvement with no additional training.

Domain Adaptation Language Modelling

From Insanely Jealous to Insanely Delicious: Computational Models for the Semantic Bleaching of English Intensifiers

no code implementations WS 2019 Yiwei Luo, Dan Jurafsky, Beth Levin

We introduce novel computational models for modeling semantic bleaching, a widespread category of change in which words become more abstract or lose elements of meaning, like the development of {``}arrive{''} from its earlier meaning {`}become at shore.

Neural Text Style Transfer via Denoising and Reranking

no code implementations WS 2019 Joseph Lee, Ziang Xie, Cindy Wang, Max Drach, Dan Jurafsky, Andrew Ng

We introduce a simple method for text style transfer that frames style transfer as denoising: we synthesize a noisy corpus and treat the source style as a noisy version of the target style.

Denoising Style Transfer +1

Let's Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms

no code implementations NAACL 2019 Diyi Yang, Jiaao Chen, Zichao Yang, Dan Jurafsky, Eduard Hovy

Modeling what makes a request persuasive - eliciting the desired response from a reader - is critical to the study of propaganda, behavioral economics, and advertising.

Sample Efficient Text Summarization Using a Single Pre-Trained Transformer

2 code implementations21 May 2019 Urvashi Khandelwal, Kevin Clark, Dan Jurafsky, Lukasz Kaiser

Language model (LM) pre-training has resulted in impressive performance and sample efficiency on a variety of language understanding tasks.

 Ranked #1 on Text Summarization on DUC 2004 Task 1 (ROUGE-2 metric)

Abstractive Text Summarization Language Modelling

Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts

1 code implementation IJCNLP 2019 Julia Kruk, Jonah Lubin, Karan Sikka, Xiao Lin, Dan Jurafsky, Ajay Divakaran

Computing author intent from multimodal data like Instagram posts requires modeling a complex relationship between text and image.

Intent Detection

Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings

1 code implementation NAACL 2019 Dorottya Demszky, Nikhil Garg, Rob Voigt, James Zou, Matthew Gentzkow, Jesse Shapiro, Dan Jurafsky

We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force.

Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts

2 code implementations EMNLP 2018 Matthew Lamm, Arun Tejasvi Chaganty, Christopher D. Manning, Dan Jurafsky, Percy Liang

To understand a sentence like "whereas only 10% of White Americans live at or below the poverty line, 28% of African Americans do" it is important not only to identify individual facts, e. g., poverty rates of distinct demographic groups, but also the higher-order relations between them, e. g., the disparity between them.

Frame Textual Analogy Parsing

Embedding Logical Queries on Knowledge Graphs

4 code implementations NeurIPS 2018 William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec

Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities.

Knowledge Graphs

Deconfounded Lexicon Induction for Interpretable Social Science

no code implementations NAACL 2018 Reid Pryzant, Kelly Shen, Dan Jurafsky, Stefan Wagner

The first uses a bifurcated architecture to separate the explanatory power of the text and confounds.

Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction

no code implementations NAACL 2018 Ziang Xie, Guillaume Genthial, Stanley Xie, Andrew Ng, Dan Jurafsky

Translation-based methods for grammar correction that directly map noisy, ungrammatical text to their clean counterparts are able to correct a broad range of errors; however, such techniques are bottlenecked by the need for a large parallel corpus of noisy and clean sentence pairs.

Denoising Machine Translation +1

Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia

no code implementations WS 2018 Dan Iter, Jong Yoon, Dan Jurafsky

Here, we present the first benchmark comparison of previously proposed coherence models for detecting symptoms of schizophrenia and evaluate their performance on a new dataset of recorded interviews between subjects and clinicians.

Sentence Embedding Sentence-Embedding +1

Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context

1 code implementation ACL 2018 Urvashi Khandelwal, He He, Peng Qi, Dan Jurafsky

We know very little about how neural language models (LM) use prior linguistic context.

Community Interaction and Conflict on the Web

no code implementations9 Mar 2018 Srijan Kumar, William L. Hamilton, Jure Leskovec, Dan Jurafsky

Here we study intercommunity interactions across 36, 000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community.

Detecting Institutional Dialog Acts in Police Traffic Stops

no code implementations TACL 2018 Vinodkumar Prabhakaran, Camilla Griffiths, Hang Su, Prateek Verma, Nelson Morgan, Jennifer L. Eberhardt, Dan Jurafsky

We apply computational dialog methods to police body-worn camera footage to model conversations between police officers and community members in traffic stops.

Speech Recognition

Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes

1 code implementation22 Nov 2017 Nikhil Garg, Londa Schiebinger, Dan Jurafsky, James Zou

Word embeddings use vectors to represent words such that the geometry between vectors captures semantic relationship between the words.

Word Embeddings

Neural Net Models of Open-domain Discourse Coherence

no code implementations EMNLP 2017 Jiwei Li, Dan Jurafsky

In this paper, we describe domain-independent neural models of discourse coherence that are capable of measuring multiple aspects of coherence in existing sentences and can maintain coherence while generating new sentences.

Abstractive Text Summarization Question Answering +2

Community Identity and User Engagement in a Multi-Community Landscape

no code implementations26 May 2017 Justine Zhang, William L. Hamilton, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky, Jure Leskovec

To this end we introduce a quantitative, language-based typology reflecting two key aspects of a community's identity: how distinctive, and how temporally dynamic it is.

A Two-stage Sieve Approach for Quote Attribution

no code implementations EACL 2017 Grace Muzny, Michael Fang, Angel Chang, Dan Jurafsky

We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3.

Loyalty in Online Communities

1 code implementation9 Mar 2017 William L. Hamilton, Justine Zhang, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky, Jure Leskovec

In this paper we operationalize loyalty as a user-community relation: users loyal to a community consistently prefer it over all others; loyal communities retain their loyal users over time.

Data Distillation for Controlling Specificity in Dialogue Generation

no code implementations22 Feb 2017 Jiwei Li, Will Monroe, Dan Jurafsky

We show that from such a set of subsystems, one can use reinforcement learning to build a system that tailors its output to different input contexts at test time.

Dialogue Generation reinforcement-learning

Learning to Decode for Future Success

no code implementations23 Jan 2017 Jiwei Li, Will Monroe, Dan Jurafsky

We introduce a simple, general strategy to manipulate the behavior of a neural decoder that enables it to generate outputs that have specific properties of interest (e. g., sequences of a pre-specified length).

Abstractive Text Summarization Decision Making +2

Adversarial Learning for Neural Dialogue Generation

7 code implementations EMNLP 2017 Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, Dan Jurafsky

In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.

Dialogue Evaluation Dialogue Generation

Understanding Neural Networks through Representation Erasure

no code implementations24 Dec 2016 Jiwei Li, Will Monroe, Dan Jurafsky

While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability.

Sentiment Analysis

A Simple, Fast Diverse Decoding Algorithm for Neural Generation

1 code implementation25 Nov 2016 Jiwei Li, Will Monroe, Dan Jurafsky

We further propose a variation that is capable of automatically adjusting its diversity decoding rates for different inputs using reinforcement learning (RL).

Abstractive Text Summarization Machine Translation +3

Citation Classification for Behavioral Analysis of a Scientific Field

no code implementations2 Sep 2016 David Jurgens, Srijan Kumar, Raine Hoover, Dan McFarland, Dan Jurafsky

Citations are an important indicator of the state of a scientific field, reflecting how authors frame their work, and influencing uptake by future scholars.

Classification Frame +1

Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change

no code implementations EMNLP 2016 William L. Hamilton, Jure Leskovec, Dan Jurafsky

Words shift in meaning for many reasons, including cultural factors like new technologies and regular linguistic processes like subjectification.

Neural Net Models for Open-Domain Discourse Coherence

1 code implementation5 Jun 2016 Jiwei Li, Dan Jurafsky

In this paper, we describe domain-independent neural models of discourse coherence that are capable of measuring multiple aspects of coherence in existing sentences and can maintain coherence while generating new sentences.

Text Generation

Deep Reinforcement Learning for Dialogue Generation

8 code implementations EMNLP 2016 Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes.

Dialogue Generation Policy Gradient Methods +1

Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change

4 code implementations ACL 2016 William L. Hamilton, Jure Leskovec, Dan Jurafsky

Understanding how words change their meanings over time is key to models of language and cultural evolution, but historical data on meaning is scarce, making theories hard to develop and test.

Diachronic Word Embeddings Word Embeddings

Mutual Information and Diverse Decoding Improve Neural Machine Translation

1 code implementation4 Jan 2016 Jiwei Li, Dan Jurafsky

We introduce an alternative objective function for neural MT that maximizes the mutual information between the source and target sentences, modeling the bi-directional dependency of sources and targets.

Machine Translation Re-Ranking +1

Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks

no code implementations18 Oct 2015 Jiwei Li, Alan Ritter, Dan Jurafsky

Inferring latent attributes of people online is an important social computing task, but requires integrating the many heterogeneous sources of information available on the web.

Community Detection Link Prediction

Visualizing and Understanding Neural Models in NLP

1 code implementation NAACL 2016 Jiwei Li, Xinlei Chen, Eduard Hovy, Dan Jurafsky

While neural networks have been successfully applied to many NLP tasks the resulting vector-based models are very difficult to interpret.

Do Multi-Sense Embeddings Improve Natural Language Understanding?

no code implementations EMNLP 2015 Jiwei Li, Dan Jurafsky

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained models of vector-space representations.

Named Entity Recognition Natural Language Understanding +3

A Hierarchical Neural Autoencoder for Paragraphs and Documents

6 code implementations IJCNLP 2015 Jiwei Li, Minh-Thang Luong, Dan Jurafsky

Natural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent networks models.

Text Generation

When Are Tree Structures Necessary for Deep Learning of Representations?

no code implementations EMNLP 2015 Jiwei Li, Minh-Thang Luong, Dan Jurafsky, Eudard Hovy

Recursive neural models, which use syntactic parse trees to recursively generate representations bottom-up, are a popular architecture.

Discourse Parsing Relation Extraction +1

Inferring User Preferences by Probabilistic Logical Reasoning over Social Networks

no code implementations11 Nov 2014 Jiwei Li, Alan Ritter, Dan Jurafsky

by building a probabilistic model that reasons over user attributes (the user's location or gender) and the social network (the user's friends and spouse), via inferences like homophily (I am more likely to like sushi if spouse or friends like sushi, I am more likely to like the Knicks if I live in New York).

Relation Extraction

How to Ask for a Favor: A Case Study on the Success of Altruistic Requests

no code implementations13 May 2014 Tim Althoff, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky

We present a case study of altruistic requests in an online community where all requests ask for the very same contribution and do not offer anything tangible in return, allowing us to disentangle what is requested from textual and social factors.

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