Cristian Danescu-Niculescu-Mizil


2020

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It Takes Two to Lie: One to Lie, and One to Listen
Denis Peskov | Benny Cheng | Ahmed Elgohary | Joe Barrow | Cristian Danescu-Niculescu-Mizil | Jordan Boyd-Graber
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Trust is implicit in many online text conversations—striking up new friendships, or asking for tech support. But trust can be betrayed through deception. We study the language and dynamics of deception in the negotiation-based game Diplomacy, where seven players compete for world domination by forging and breaking alliances with each other. Our study with players from the Diplomacy community gathers 17,289 messages annotated by the sender for their intended truthfulness and by the receiver for their perceived truthfulness. Unlike existing datasets, this captures deception in long-lasting relationships, where the interlocutors strategically combine truth with lies to advance objectives. A model that uses power dynamics and conversational contexts can predict when a lie occurs nearly as well as human players.

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Balancing Objectives in Counseling Conversations: Advancing Forwards or Looking Backwards
Justine Zhang | Cristian Danescu-Niculescu-Mizil
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Throughout a conversation, participants make choices that can orient the flow of the interaction. Such choices are particularly salient in the consequential domain of crisis counseling, where a difficulty for counselors is balancing between two key objectives: advancing the conversation towards a resolution, and empathetically addressing the crisis situation. In this work, we develop an unsupervised methodology to quantify how counselors manage this balance. Our main intuition is that if an utterance can only receive a narrow range of appropriate replies, then its likely aim is to advance the conversation forwards, towards a target within that range. Likewise, an utterance that can only appropriately follow a narrow range of possible utterances is likely aimed backwards at addressing a specific situation within that range. By applying this intuition, we can map each utterance to a continuous orientation axis that captures the degree to which it is intended to direct the flow of the conversation forwards or backwards. This unsupervised method allows us to characterize counselor behaviors in a large dataset of crisis counseling conversations, where we show that known counseling strategies intuitively align with this axis. We also illustrate how our measure can be indicative of a conversation’s progress, as well as its effectiveness.

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Facilitating the Communication of Politeness through Fine-Grained Paraphrasing
Liye Fu | Susan Fussell | Cristian Danescu-Niculescu-Mizil
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker’s intentions and the listener’s perceptions in both cases.

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ConvoKit: A Toolkit for the Analysis of Conversations
Jonathan P. Chang | Caleb Chiam | Liye Fu | Andrew Wang | Justine Zhang | Cristian Danescu-Niculescu-Mizil
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

This paper describes the design and functionality of ConvoKit, an open-source toolkit for analyzing conversations and the social interactions embedded within. ConvoKit provides an unified framework for representing and manipulating conversational data, as well as a large and diverse collection of conversational datasets. By providing an intuitive interface for exploring and interacting with conversational data, this toolkit lowers the technical barriers for the broad adoption of computational methods for conversational analysis.

2019

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Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop
Jonathan P. Chang | Cristian Danescu-Niculescu-Mizil
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Online discussions often derail into toxic exchanges between participants. Recent efforts mostly focused on detecting antisocial behavior after the fact, by analyzing single comments in isolation. To provide more timely notice to human moderators, a system needs to preemptively detect that a conversation is heading towards derailment before it actually turns toxic. This means modeling derailment as an emerging property of a conversation rather than as an isolated utterance-level event. Forecasting emerging conversational properties, however, poses several inherent modeling challenges. First, since conversations are dynamic, a forecasting model needs to capture the flow of the discussion, rather than properties of individual comments. Second, real conversations have an unknown horizon: they can end or derail at any time; thus a practical forecasting model needs to assess the risk in an online fashion, as the conversation develops. In this work we introduce a conversational forecasting model that learns an unsupervised representation of conversational dynamics and exploits it to predict future derailment as the conversation develops. By applying this model to two new diverse datasets of online conversations with labels for antisocial events, we show that it outperforms state-of-the-art systems at forecasting derailment.

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Finding Your Voice: The Linguistic Development of Mental Health Counselors
Justine Zhang | Robert Filbin | Christine Morrison | Jaclyn Weiser | Cristian Danescu-Niculescu-Mizil
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Mental health counseling is an enterprise with profound societal importance where conversations play a primary role. In order to acquire the conversational skills needed to face a challenging range of situations, mental health counselors must rely on training and on continued experience with actual clients. However, in the absence of large scale longitudinal studies, the nature and significance of this developmental process remain unclear. For example, prior literature suggests that experience might not translate into consequential changes in counselor behavior. This has led some to even argue that counseling is a profession without expertise. In this work, we develop a computational framework to quantify the extent to which individuals change their linguistic behavior with experience and to study the nature of this evolution. We use our framework to conduct a large longitudinal study of mental health counseling conversations, tracking over 3,400 counselors across their tenure. We reveal that overall, counselors do indeed change their conversational behavior to become more diverse across interactions, developing an individual voice that distinguishes them from other counselors. Furthermore, a finer-grained investigation shows that the rate and nature of this diversification vary across functionally different conversational components.

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Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives
Liye Fu | Jonathan P. Chang | Cristian Danescu-Niculescu-Mizil
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

People often share personal narratives in order to seek advice from others. To properly infer the narrator’s intention, one needs to apply a certain degree of common sense and social intuition. To test the capabilities of NLP systems to recover such intuition, we introduce the new task of inferring what is the advice-seeking goal behind a personal narrative. We formulate this as a cloze test, where the goal is to identify which of two advice-seeking questions was removed from a given narrative. The main challenge in constructing this task is finding pairs of semantically plausible advice-seeking questions for given narratives. To address this challenge, we devise a method that exploits commonalities in experiences people share online to automatically extract pairs of questions that are appropriate candidates for the cloze task. This results in a dataset of over 20,000 personal narratives, each matched with a pair of related advice-seeking questions: one actually intended by the narrator, and the other one not. The dataset covers a very broad array of human experiences, from dating, to career options, to stolen iPads. We use human annotation to determine the degree to which the task relies on common sense and social intuition in addition to a semantic understanding of the narrative. By introducing several baselines for this new task we demonstrate its feasibility and identify avenues for better modeling the intention of the narrator.

2018

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WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community
Yiqing Hua | Cristian Danescu-Niculescu-Mizil | Dario Taraborelli | Nithum Thain | Jeffery Sorensen | Lucas Dixon
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present a corpus that encompasses the complete history of conversations between contributors to Wikipedia, one of the largest online collaborative communities. By recording the intermediate states of conversations - including not only comments and replies, but also their modifications, deletions and restorations - this data offers an unprecedented view of online conversation. Our framework is designed to be language agnostic, and we show that it extracts high quality data in both Chinese and English. This level of detail supports new research questions pertaining to the process (and challenges) of large-scale online collaboration. We illustrate the corpus’ potential with two case studies on English Wikipedia that highlight new perspectives on earlier work. First, we explore how a person’s conversational behavior depends on how they relate to the discussion’s venue. Second, we show that community moderation of toxic behavior happens at a higher rate than previously estimated.

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Conversations Gone Awry: Detecting Early Signs of Conversational Failure
Justine Zhang | Jonathan Chang | Cristian Danescu-Niculescu-Mizil | Lucas Dixon | Yiqing Hua | Dario Taraborelli | Nithum Thain
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

One of the main challenges online social systems face is the prevalence of antisocial behavior, such as harassment and personal attacks. In this work, we introduce the task of predicting from the very start of a conversation whether it will get out of hand. As opposed to detecting undesirable behavior after the fact, this task aims to enable early, actionable prediction at a time when the conversation might still be salvaged. To this end, we develop a framework for capturing pragmatic devices—such as politeness strategies and rhetorical prompts—used to start a conversation, and analyze their relation to its future trajectory. Applying this framework in a controlled setting, we demonstrate the feasibility of detecting early warning signs of antisocial behavior in online discussions.

2017

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Asking too much? The rhetorical role of questions in political discourse
Justine Zhang | Arthur Spirling | Cristian Danescu-Niculescu-Mizil
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Questions play a prominent role in social interactions, performing rhetorical functions that go beyond that of simple informational exchange. The surface form of a question can signal the intention and background of the person asking it, as well as the nature of their relation with the interlocutor. While the informational nature of questions has been extensively examined in the context of question-answering applications, their rhetorical aspects have been largely understudied. In this work we introduce an unsupervised methodology for extracting surface motifs that recur in questions, and for grouping them according to their latent rhetorical role. By applying this framework to the setting of question sessions in the UK parliament, we show that the resulting typology encodes key aspects of the political discourse—such as the bifurcation in questioning behavior between government and opposition parties—and reveals new insights into the effects of a legislator’s tenure and political career ambitions.

2016

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Conversational Flow in Oxford-style Debates
Justine Zhang | Ravi Kumar | Sujith Ravi | Cristian Danescu-Niculescu-Mizil
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Conversational Markers of Constructive Discussions
Vlad Niculae | Cristian Danescu-Niculescu-Mizil
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game
Vlad Niculae | Srijan Kumar | Jordan Boyd-Graber | Cristian Danescu-Niculescu-Mizil
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science
Cristian Danescu-Niculescu-Mizil | Jacob Eisenstein | Kathleen McKeown | Noah A. Smith
Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science

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Brighter than Gold: Figurative Language in User Generated Comparisons
Vlad Niculae | Cristian Danescu-Niculescu-Mizil
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Proceedings of the Workshop on Language Analysis in Social Media
Cristian Danescu-Niculescu-Mizil | Atefeh Farzindar | Michael Gamon | Diana Inkpen | Meena Nagarajan
Proceedings of the Workshop on Language Analysis in Social Media

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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)

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Linguistic Models for Analyzing and Detecting Biased Language
Marta Recasens | Cristian Danescu-Niculescu-Mizil | Dan Jurafsky
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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You Had Me at Hello: How Phrasing Affects Memorability
Cristian Danescu-Niculescu-Mizil | Justin Cheng | Jon Kleinberg | Lillian Lee
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Hedge Detection as a Lens on Framing in the GMO Debates: A Position Paper
Eunsol Choi | Chenhao Tan | Lillian Lee | Cristian Danescu-Niculescu-Mizil | Jennifer Spindel
Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics

2011

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Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs
Cristian Danescu-Niculescu-Mizil | Lillian Lee
Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics

2010

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For the sake of simplicity: Unsupervised extraction of lexical simplifications from Wikipedia
Mark Yatskar | Bo Pang | Cristian Danescu-Niculescu-Mizil | Lillian Lee
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Don’t ‘Have a Clue’? Unsupervised Co-Learning of Downward-Entailing Operators.
Cristian Danescu-Niculescu-Mizil | Lillian Lee
Proceedings of the ACL 2010 Conference Short Papers

2009

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Without a ’doubt’? Unsupervised Discovery of Downward-Entailing Operators
Cristian Danescu-Niculescu-Mizil | Lillian Lee | Richard Ducott
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics