Verónica Pérez-Rosas

Also published as: Veronica Perez-Rosas


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Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for Counselor Reflection Generation
Do June Min | Veronica Perez-Rosas | Ken Resnicow | Rada Mihalcea
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text qualities for natural language generation. We focus on the task of counselor reflection generation, where we optimize the generators to simultaneously improve the fluency, coherence, and reflection quality of generated counselor responses. We introduce two novel bandit methods, DynaOpt and C-DynaOpt, which rely on the broad strategy of combining rewards into a single value and optimizing them simultaneously. Specifically, we employ non-contextual and contextual multi-arm bandits to dynamically adjust multiple reward weights during training. Through automatic and manual evaluations, we show that our proposed techniques, DynaOpt and C-DynaOpt, outperform existing naive and bandit baselines, showcasing their potential for enhancing language models.

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Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models
Oana Ignat | Zhijing Jin | Artem Abzaliev | Laura Biester | Santiago Castro | Naihao Deng | Xinyi Gao | Aylin Ece Gunal | Jacky He | Ashkan Kazemi | Muhammad Khalifa | Namho Koh | Andrew Lee | Siyang Liu | Do June Min | Shinka Mori | Joan C. Nwatu | Veronica Perez-Rosas | Siqi Shen | Zekun Wang | Winston Wu | Rada Mihalcea
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Recent progress in large language models (LLMs) has enabled the deployment of many generative NLP applications. At the same time, it has also led to a misleading public discourse that “it’s all been solved.” Not surprisingly, this has, in turn, made many NLP researchers – especially those at the beginning of their careers – worry about what NLP research area they should focus on. Has it all been solved, or what remaining questions can we work on regardless of LLMs? To address this question, this paper compiles NLP research directions rich for exploration. We identify fourteen different research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. While we identify many research areas, many others exist; we do not cover areas currently addressed by LLMs, but where LLMs lag behind in performance or those focused on LLM development. We welcome suggestions for other research directions to include:


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VERVE: Template-based ReflectiVE Rewriting for MotiVational IntErviewing
Do June Min | Veronica Perez-Rosas | Ken Resnicow | Rada Mihalcea
Findings of the Association for Computational Linguistics: EMNLP 2023

Reflective listening is a fundamental skill that counselors must acquire to achieve proficiency in motivational interviewing (MI). It involves responding in a manner that acknowledges and explores the meaning of what the client has expressed in the conversation. In this work, we introduce the task of counseling response rewriting, which transforms non-reflective statements into reflective responses. We introduce VERVE, a template-based rewriting system with paraphrase-augmented training and adaptive template updating. VERVE first creates a template by identifying and filtering out tokens that are not relevant to reflections and constructs a reflective response using the template. Paraphrase-augmented training allows the model to learn less-strict fillings of masked spans, and adaptive template updating helps discover effective templates for rewriting without significantly removing the original content. Using both automatic and human evaluations, we compare our method against text rewriting baselines and show that our framework is effective in turning non-reflective statements into more reflective responses while achieving a good content preservation-reflection style trade-off.

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Query Rewriting for Effective Misinformation Discovery
Ashkan Kazemi | Artem Abzaliev | Naihao Deng | Rui Hou | Scott Hale | Veronica Perez-Rosas | Rada Mihalcea
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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Navigating Data Scarcity: Pretraining for Medical Utterance Classification
Do June Min | Veronica Perez-Rosas | Rada Mihalcea
Proceedings of the 5th Clinical Natural Language Processing Workshop

Pretrained language models leverage self-supervised learning to use large amounts of unlabeled text for learning contextual representations of sequences. However, in the domain of medical conversations, the availability of large, public datasets is limited due to issues of privacy and data management. In this paper, we study the effectiveness of dialog-aware pretraining objectives and multiphase training in using unlabeled data to improve LMs training for medical utterance classification. The objectives of pretraining for dialog awareness involve tasks that take into account the structure of conversations, including features such as turn-taking and the roles of speakers. The multiphase training process uses unannotated data in a sequence that prioritizes similarities and connections between different domains. We empirically evaluate these methods on conversational dialog classification tasks in the medical and counseling domains, and find that multiphase training can help achieve higher performance than standard pretraining or finetuning.


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PAIR: Prompt-Aware margIn Ranking for Counselor Reflection Scoring in Motivational Interviewing
Do June Min | Verónica Pérez-Rosas | Kenneth Resnicow | Rada Mihalcea
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Counselor reflection is a core verbal skill used by mental health counselors to express understanding and affirmation of the client’s experience and concerns. In this paper, we propose a system for the analysis of counselor reflections. Specifically, our system takes as input one dialog turn containing a client prompt and a counselor response, and outputs a score indicating the level of reflection in the counselor response. We compile a dataset consisting of different levels of reflective listening skills, and propose the Prompt-Aware margIn Ranking (PAIR) framework that contrasts positive and negative prompt and response pairs using specially designed multi-gap and prompt-aware margin ranking losses. Through empirical evaluations and deployment of our system in a real-life educational environment, we show that our analysis model outperforms several baselines on different metrics, and can be used to provide useful feedback to counseling trainees.

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Leveraging Similar Users for Personalized Language Modeling with Limited Data
Charles Welch | Chenxi Gu | Jonathan K. Kummerfeld | Veronica Perez-Rosas | Rada Mihalcea
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Personalized language models are designed and trained to capture language patterns specific to individual users. This makes them more accurate at predicting what a user will write. However, when a new user joins a platform and not enough text is available, it is harder to build effective personalized language models. We propose a solution for this problem, using a model trained on users that are similar to a new user. In this paper, we explore strategies for finding the similarity between new users and existing ones and methods for using the data from existing users who are a good match. We further explore the trade-off between available data for new users and how well their language can be modeled.

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Knowledge Enhanced Reflection Generation for Counseling Dialogues
Siqi Shen | Veronica Perez-Rosas | Charles Welch | Soujanya Poria | Rada Mihalcea
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we study the effect of commonsense and domain knowledge while generating responses in counseling conversations using retrieval and generative methods for knowledge integration. We propose a pipeline that collects domain knowledge through web mining, and show that retrieval from both domain-specific and commonsense knowledge bases improves the quality of generated responses. We also present a model that incorporates knowledge generated by COMET using soft positional encoding and masked self-attention. We show that both retrieved and COMET-generated knowledge improve the system’s performance as measured by automatic metrics and also by human evaluation. Lastly, we present a comparative study on the types of knowledge encoded by our system showing that causal and intentional relationships benefit the generation task more than other types of commonsense relations.


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Exploring Self-Identified Counseling Expertise in Online Support Forums
Allison Lahnala | Yuntian Zhao | Charles Welch | Jonathan K. Kummerfeld | Lawrence C An | Kenneth Resnicow | Rada Mihalcea | Verónica Pérez-Rosas
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News
Ashkan Kazemi | Zehua Li | Verónica Pérez-Rosas | Rada Mihalcea
Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda

In this paper, we explore the construction of natural language explanations for news claims, with the goal of assisting fact-checking and news evaluation applications. We experiment with two methods: (1) an extractive method based on Biased TextRank – a resource-effective unsupervised graph-based algorithm for content extraction; and (2) an abstractive method based on the GPT-2 language model. We perform comparative evaluations on two misinformation datasets in the political and health news domains, and find that the extractive method shows the most promise.

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Evaluating Automatic Speech Recognition Quality and Its Impact on Counselor Utterance Coding
Do June Min | Verónica Pérez-Rosas | Rada Mihalcea
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

Automatic speech recognition (ASR) is a crucial step in many natural language processing (NLP) applications, as often available data consists mainly of raw speech. Since the result of the ASR step is considered as a meaningful, informative input to later steps in the NLP pipeline, it is important to understand the behavior and failure mode of this step. In this work, we analyze the quality of ASR in the psychotherapy domain, using motivational interviewing conversations between therapists and clients. We conduct domain agnostic and domain-relevant evaluations using standard evaluation metrics and also identify domain-relevant keywords in the ASR output. Moreover, we empirically study the effect of mixing ASR and manual data during the training of a downstream NLP model, and also demonstrate how additional local context can help alleviate the error introduced by noisy ASR transcripts.


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Inferring Social Media Users’ Mental Health Status from Multimodal Information
Zhentao Xu | Verónica Pérez-Rosas | Rada Mihalcea
Proceedings of the Twelfth Language Resources and Evaluation Conference

Worldwide, an increasing number of people are suffering from mental health disorders such as depression and anxiety. In the United States alone, one in every four adults suffers from a mental health condition, which makes mental health a pressing concern. In this paper, we explore the use of multimodal cues present in social media posts to predict users’ mental health status. Specifically, we focus on identifying social media activity that either indicates a mental health condition or its onset. We collect posts from Flickr and apply a multimodal approach that consists of jointly analyzing language, visual, and metadata cues and their relation to mental health. We conduct several classification experiments aiming to discriminate between (1) healthy users and users affected by a mental health illness; and (2) healthy users and users prone to mental illness. Our experimental results indicate that using multiple modalities can improve the performance of this classification task as compared to the use of one modality at a time, and can provide important cues into a user’s mental status.

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Biased TextRank: Unsupervised Graph-Based Content Extraction
Ashkan Kazemi | Verónica Pérez-Rosas | Rada Mihalcea
Proceedings of the 28th International Conference on Computational Linguistics

We introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to their relevance to an input “focus.” Biased TextRank enables focused content extraction for text by modifying the random restarts in the execution of TextRank. The random restart probabilities are assigned based on the relevance of the graph nodes to the focus of the task. We present two applications of Biased TextRank: focused summarization and explanation extraction, and show that our algorithm leads to improved performance on two different datasets by significant ROUGE-N score margins. Much like its predecessor, Biased TextRank is unsupervised, easy to implement and orders of magnitude faster and lighter than current state-of-the-art Natural Language Processing methods for similar tasks.

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Exploring the Value of Personalized Word Embeddings
Charles Welch | Jonathan K. Kummerfeld | Verónica Pérez-Rosas | Rada Mihalcea
Proceedings of the 28th International Conference on Computational Linguistics

In this paper, we introduce personalized word embeddings, and examine their value for language modeling. We compare the performance of our proposed prediction model when using personalized versus generic word representations, and study how these representations can be leveraged for improved performance. We provide insight into what types of words can be more accurately predicted when building personalized models. Our results show that a subset of words belonging to specific psycholinguistic categories tend to vary more in their representations across users and that combining generic and personalized word embeddings yields the best performance, with a 4.7% relative reduction in perplexity. Additionally, we show that a language model using personalized word embeddings can be effectively used for authorship attribution.

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Expressive Interviewing: A Conversational System for Coping with COVID-19
Charles Welch | Allison Lahnala | Veronica Perez-Rosas | Siqi Shen | Sarah Seraj | Larry An | Kenneth Resnicow | James Pennebaker | Rada Mihalcea
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprecedented challenges, there are increasing concerns over social isolation and mental health. We introduce Expressive Interviewing – an interview-style conversational system that draws on ideas from motivational interviewing and expressive writing. Expressive Interviewing seeks to encourage users to express their thoughts and feelings through writing by asking them questions about how COVID-19 has impacted their lives. We present relevant aspects of the system’s design and implementation as well as quantitative and qualitative analyses of user interactions with the system. In addition, we conduct a comparative evaluation with a general purpose dialogue system for mental health that shows our system potential in helping users to cope with COVID-19 issues.

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Counseling-Style Reflection Generation Using Generative Pretrained Transformers with Augmented Context
Siqi Shen | Charles Welch | Rada Mihalcea | Verónica Pérez-Rosas
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

We introduce a counseling dialogue system that seeks to assist counselors while they are learning and refining their counseling skills. The system generates counselors’reflections – i.e., responses that reflect back on what the client has said given the dialogue history. Our method builds upon the new generative pretrained transformer architecture and enhances it with context augmentation techniques inspired by traditional strategies used during counselor training. Through a set of comparative experiments, we show that the system that incorporates these strategies performs better in the reflection generation task than a system that is just fine-tuned with counseling conversations. To confirm our findings, we present a human evaluation study that shows that our system generates naturally-looking reflections that are also stylistically and grammatically correct.

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Compositional Demographic Word Embeddings
Charles Welch | Jonathan K. Kummerfeld | Verónica Pérez-Rosas | Rada Mihalcea
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Word embeddings are usually derived from corpora containing text from many individuals, thus leading to general purpose representations rather than individually personalized representations. While personalized embeddings can be useful to improve language model performance and other language processing tasks, they can only be computed for people with a large amount of longitudinal data, which is not the case for new users. We propose a new form of personalized word embeddings that use demographic-specific word representations derived compositionally from full or partial demographic information for a user (i.e., gender, age, location, religion). We show that the resulting demographic-aware word representations outperform generic word representations on two tasks for English: language modeling and word associations. We further explore the trade-off between the number of available attributes and their relative effectiveness and discuss the ethical implications of using them.


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Box of Lies: Multimodal Deception Detection in Dialogues
Felix Soldner | Verónica Pérez-Rosas | Rada Mihalcea
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)

Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection. In this paper, we address the task of detecting multimodal deceptive cues during conversational dialogues. We introduce a multimodal dataset containing deceptive conversations between participants playing the Box of Lies game from The Tonight Show Starring Jimmy Fallon, in which they try to guess whether an object description provided by their opponent is deceptive or not. We conduct annotations of multimodal communication behaviors, including facial and linguistic behaviors, and derive several learning features based on these annotations. Initial classification experiments show promising results, performing well above both a random and a human baseline, and reaching up to 69% accuracy in distinguishing deceptive and truthful behaviors.

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What Makes a Good Counselor? Learning to Distinguish between High-quality and Low-quality Counseling Conversations
Verónica Pérez-Rosas | Xinyi Wu | Kenneth Resnicow | Rada Mihalcea
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The quality of a counseling intervention relies highly on the active collaboration between clients and counselors. In this paper, we explore several linguistic aspects of the collaboration process occurring during counseling conversations. Specifically, we address the differences between high-quality and low-quality counseling. Our approach examines participants’ turn-by-turn interaction, their linguistic alignment, the sentiment expressed by speakers during the conversation, as well as the different topics being discussed. Our results suggest important language differences in low- and high-quality counseling, which we further use to derive linguistic features able to capture the differences between the two groups. These features are then used to build automatic classifiers that can predict counseling quality with accuracies of up to 88%.

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Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)
Santiago Castro | Devamanyu Hazarika | Verónica Pérez-Rosas | Roger Zimmermann | Rada Mihalcea | Soujanya Poria
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the recent work in sarcasm detection has been carried out on textual data. In this paper, we argue that incorporating multimodal cues can improve the automatic classification of sarcasm. As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows. MUStARD consists of audiovisual utterances annotated with sarcasm labels. Each utterance is accompanied by its context of historical utterances in the dialogue, which provides additional information on the scenario where the utterance occurs. Our initial results show that the use of multimodal information can reduce the relative error rate of sarcasm detection by up to 12.9% in F-score when compared to the use of individual modalities. The full dataset is publicly available for use at


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Analyzing the Quality of Counseling Conversations: the Tell-Tale Signs of High-quality Counseling
Verónica Pérez-Rosas | Xuetong Sun | Christy Li | Yuchen Wang | Kenneth Resnicow | Rada Mihalcea
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Automatic Detection of Fake News
Verónica Pérez-Rosas | Bennett Kleinberg | Alexandra Lefevre | Rada Mihalcea
Proceedings of the 27th International Conference on Computational Linguistics

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analyses on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors, and show that we can achieve accuracies of up to 76%. In addition, we provide comparative analyses of the automatic and manual identification of fake news.


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Predicting Counselor Behaviors in Motivational Interviewing Encounters
Verónica Pérez-Rosas | Rada Mihalcea | Kenneth Resnicow | Satinder Singh | Lawrence An | Kathy J. Goggin | Delwyn Catley
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

As the number of people receiving psycho-therapeutic treatment increases, the automatic evaluation of counseling practice arises as an important challenge in the clinical domain. In this paper, we address the automatic evaluation of counseling performance by analyzing counselors’ language during their interaction with clients. In particular, we present a model towards the automation of Motivational Interviewing (MI) coding, which is the current gold standard to evaluate MI counseling. First, we build a dataset of hand labeled MI encounters; second, we use text-based methods to extract and analyze linguistic patterns associated with counselor behaviors; and third, we develop an automatic system to predict these behaviors. We introduce a new set of features based on semantic information and syntactic patterns, and show that they lead to accuracy figures of up to 90%, which represent a significant improvement with respect to features used in the past.

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Identity Deception Detection
Verónica Pérez-Rosas | Quincy Davenport | Anna Mengdan Dai | Mohamed Abouelenien | Rada Mihalcea
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This paper addresses the task of detecting identity deception in language. Using a novel identity deception dataset, consisting of real and portrayed identities from 600 individuals, we show that we can build accurate identity detectors targeting both age and gender, with accuracies of up to 88. We also perform an analysis of the linguistic patterns used in identity deception, which lead to interesting insights into identity portrayers.

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Understanding and Predicting Empathic Behavior in Counseling Therapy
Verónica Pérez-Rosas | Rada Mihalcea | Kenneth Resnicow | Satinder Singh | Lawrence An
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Counselor empathy is associated with better outcomes in psychology and behavioral counseling. In this paper, we explore several aspects pertaining to counseling interaction dynamics and their relation to counselor empathy during motivational interviewing encounters. Particularly, we analyze aspects such as participants’ engagement, participants’ verbal and nonverbal accommodation, as well as topics being discussed during the conversation, with the final goal of identifying linguistic and acoustic markers of counselor empathy. We also show how we can use these findings alongside other raw linguistic and acoustic features to build accurate counselor empathy classifiers with accuracies of up to 80%.


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Building a Motivational Interviewing Dataset
Verónica Pérez-Rosas | Rada Mihalcea | Kenneth Resnicow | Satinder Singh | Lawrence An
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology


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Experiments in Open Domain Deception Detection
Verónica Pérez-Rosas | Rada Mihalcea
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Verbal and Nonverbal Clues for Real-life Deception Detection
Verónica Pérez-Rosas | Mohamed Abouelenien | Rada Mihalcea | Yao Xiao | CJ Linton | Mihai Burzo
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


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Cross-cultural Deception Detection
Verónica Pérez-Rosas | Rada Mihalcea
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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A Multimodal Dataset for Deception Detection
Verónica Pérez-Rosas | Rada Mihalcea | Alexis Narvaez | Mihai Burzo
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents the construction of a multimodal dataset for deception detection, including physiological, thermal, and visual responses of human subjects under three deceptive scenarios. We present the experimental protocol, as well as the data acquisition process. To evaluate the usefulness of the dataset for the task of deception detection, we present a statistical analysis of the physiological and thermal modalities associated with the deceptive and truthful conditions. Initial results show that physiological and thermal responses can differentiate between deceptive and truthful states.


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Utterance-Level Multimodal Sentiment Analysis
Verónica Pérez-Rosas | Rada Mihalcea | Louis-Philippe Morency
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Learning Sentiment Lexicons in Spanish
Verónica Pérez-Rosas | Carmen Banea | Rada Mihalcea
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In this paper we present a framework to derive sentiment lexicons in a target language by using manually or automatically annotated data available in an electronic resource rich language, such as English. We show that bridging the language gap using the multilingual sense-level aligned WordNet structure allows us to generate a high accuracy (90%) polarity lexicon comprising 1,347 entries, and a disjoint lower accuracy (74%) one encompassing 2,496 words. By using an LSA-based vectorial expansion for the generated lexicons, we are able to obtain an average F-measure of 66% in the target language. This implies that the lexicons could be used to bootstrap higher-coverage lexicons using in-language resources.