Search Results for author: Rada Mihalcea

Found 128 papers, 35 papers with code

Modality-specific Learning Rates for Effective Multimodal Additive Late-fusion

no code implementations Findings (ACL) 2022 Yiqun Yao, Rada Mihalcea

Moreover, for different modalities, the best unimodal models may work under significantly different learning rates due to the nature of the modality and the computational flow of the model; thus, selecting a global learning rate for late-fusion models can result in a vanishing gradient for some modalities.

Knowledge Enhanced Reflection Generation for Counseling Dialogues

no code implementations ACL 2022 Siqi Shen, Veronica Perez-Rosas, Charles Welch, Soujanya Poria, Rada Mihalcea

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.

Evaluating Automatic Speech Recognition Quality and Its Impact on Counselor Utterance Coding

no code implementations NAACL (CLPsych) 2021 Do June Min, Verónica Pérez-Rosas, Rada Mihalcea

Automatic speech recognition (ASR) is a crucial step in many natural language processing (NLP) applications, as often available data consists mainly of raw speech.

Automatic Speech Recognition

Counseling-Style Reflection Generation Using Generative Pretrained Transformers with Augmented Context

no code implementations SIGDIAL (ACL) 2020 Siqi Shen, Charles Welch, Rada Mihalcea, Verónica Pérez-Rosas

We introduce a counseling dialogue system that seeks to assist counselors while they are learning and refining their counseling skills.

Logical Fallacy Detection

1 code implementation28 Feb 2022 Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schölkopf

In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate).

Language Modelling Misinformation

Matching Tweets With Applicable Fact-Checks Across Languages

no code implementations14 Feb 2022 Ashkan Kazemi, Zehua Li, Verónica Pérez-Rosas, Scott A. Hale, Rada Mihalcea

We conduct both classification and retrieval experiments, in monolingual (English only), multilingual (Spanish, Portuguese), and cross-lingual (Hindi-English) settings using multilingual transformer models such as XLM-RoBERTa and multilingual embeddings such as LaBSE and SBERT.

Fact Checking

How Well Do You Know Your Audience? Reader-aware Question Generation

no code implementations16 Oct 2021 Ian Stewart, Rada Mihalcea

When writing, a person may need to anticipate questions from their readers, but different types of readers may ask very different types of questions.

Question Generation Text Generation

Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health

1 code implementation Findings (EMNLP) 2021 Andrew Lee, Jonathan K. Kummerfeld, Lawrence C. An, Rada Mihalcea

Many statistical models have high accuracy on test benchmarks, but are not explainable, struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily integrate domain expertise.

Classification

Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication

1 code implementation22 Jun 2021 Navonil Majumder, Deepanway Ghosal, Devamanyu Hazarika, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria

We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics.

Empathetic Response Generation Passage Retrieval +1

How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact

2 code implementations Findings (ACL) 2021 Zhijing Jin, Geeticka Chauhan, Brian Tse, Mrinmaya Sachan, Rada Mihalcea

We lay the foundations via the moral philosophy definition of social good, propose a framework to evaluate the direct and indirect real-world impact of NLP tasks, and adopt the methodology of global priorities research to identify priority causes for NLP research.

MUSER: MUltimodal Stress Detection using Emotion Recognition as an Auxiliary Task

no code implementations NAACL 2021 Yiqun Yao, Michalis Papakostas, Mihai Burzo, Mohamed Abouelenien, Rada Mihalcea

The capability to automatically detect human stress can benefit artificial intelligent agents involved in affective computing and human-computer interaction.

Emotion Recognition Multi-Task Learning

Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News

no code implementations NAACL (NLP4IF) 2021 Ashkan Kazemi, Zehua Li, Verónica Pérez-Rosas, Rada Mihalcea

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.

Fact Checking Language Modelling +1

FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework

1 code implementation ACL 2022 Santiago Castro, Ruoyao Wang, Pingxuan Huang, Ian Stewart, Oana Ignat, Nan Liu, Jonathan C. Stroud, Rada Mihalcea

We propose fill-in-the-blanks as a video understanding evaluation framework and introduce FIBER -- a novel dataset consisting of 28, 000 videos and descriptions in support of this evaluation framework.

Language Modelling Multiple-choice +4

Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction

no code implementations4 Feb 2021 Allison Lahnala, Gauri Kambhatla, Jiajun Peng, Matthew Whitehead, Gillian Minnehan, Eric Guldan, Jonathan K. Kummerfeld, Anıl Çamcı, Rada Mihalcea

In the first case study, we demonstrate that using chord embeddings in a next chord prediction task yields predictions that more closely match those by experienced musicians.

White Paper: Challenges and Considerations for the Creation of a Large Labelled Repository of Online Videos with Questionable Content

no code implementations25 Jan 2021 Thamar Solorio, Mahsa Shafaei, Christos Smailis, Mona Diab, Theodore Giannakopoulos, Heng Ji, Yang Liu, Rada Mihalcea, Smaranda Muresan, Ioannis Kakadiaris

This white paper presents a summary of the discussions regarding critical considerations to develop an extensive repository of online videos annotated with labels indicating questionable content.

Recognizing Emotion Cause in Conversations

1 code implementation22 Dec 2020 Soujanya Poria, Navonil Majumder, Devamanyu Hazarika, Deepanway Ghosal, Rishabh Bhardwaj, Samson Yu Bai Jian, Pengfei Hong, Romila Ghosh, Abhinaba Roy, Niyati Chhaya, Alexander Gelbukh, Rada Mihalcea

We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong Transformer-based baselines.

Causal Emotion Entailment Emotion Cause Extraction

Improving Zero Shot Learning Baselines with Commonsense Knowledge

no code implementations11 Dec 2020 Abhinaba Roy, Deepanway Ghosal, Erik Cambria, Navonil Majumder, Rada Mihalcea, Soujanya Poria

Zero shot learning -- the problem of training and testing on a completely disjoint set of classes -- relies greatly on its ability to transfer knowledge from train classes to test classes.

Word Embeddings Zero-Shot Learning

Building Location Embeddings from Physical Trajectories and Textual Representations

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Laura Biester, Carmen Banea, Rada Mihalcea

Word embedding methods have become the de-facto way to represent words, having been successfully applied to a wide array of natural language processing tasks.

Exploring the Value of Personalized Word Embeddings

no code implementations COLING 2020 Charles Welch, Jonathan K. Kummerfeld, Verónica Pérez-Rosas, Rada Mihalcea

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.

Language Modelling Word Embeddings

Biased TextRank: Unsupervised Graph-Based Content Extraction

no code implementations COLING 2020 Ashkan Kazemi, Verónica Pérez-Rosas, Rada Mihalcea

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."

Deep Learning for Text Style Transfer: A Survey

2 code implementations1 Nov 2020 Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea

Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others.

Style Transfer Text Attribute Transfer +1

Compositional Demographic Word Embeddings

1 code implementation EMNLP 2020 Charles Welch, Jonathan K. Kummerfeld, Verónica Pérez-Rosas, Rada Mihalcea

Word embeddings are usually derived from corpora containing text from many individuals, thus leading to general purpose representations rather than individually personalized representations.

Language Modelling Word Embeddings

MIME: MIMicking Emotions for Empathetic Response Generation

1 code implementation EMNLP 2020 Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria

Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly.

Empathetic Response Generation Response Generation

Improving Low Compute Language Modeling with In-Domain Embedding Initialisation

1 code implementation EMNLP 2020 Charles Welch, Rada Mihalcea, Jonathan K. Kummerfeld

In the process, we show that the standard convention of tying input and output embeddings does not improve perplexity when initializing with embeddings trained on in-domain data.

Language Modelling

Quantifying the Effects of COVID-19 on Mental Health Support Forums

no code implementations EMNLP (NLP-COVID19) 2020 Laura Biester, Katie Matton, Janarthanan Rajendran, Emily Mower Provost, Rada Mihalcea

The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health.

Expressive Interviewing: A Conversational System for Coping with COVID-19

no code implementations EMNLP (NLP-COVID19) 2020 Charles Welch, Allison Lahnala, Verónica Pérez-Rosas, Siqi Shen, Sarah Seraj, Larry An, Kenneth Resnicow, James Pennebaker, Rada Mihalcea

The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability.

KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis

1 code implementation ACL 2020 Deepanway Ghosal, Devamanyu Hazarika, Abhinaba Roy, Navonil Majumder, Rada Mihalcea, Soujanya Poria

Cross-domain sentiment analysis has received significant attention in recent years, prompted by the need to combat the domain gap between different applications that make use of sentiment analysis.

Domain Adaptation Sentiment Analysis

Inferring Social Media Users' Mental Health Status from Multimodal Information

no code implementations LREC 2020 Zhentao Xu, Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea

In this paper, we explore the use of multimodal cues present in social media posts to predict users{'} mental health status.

General Classification

Small Town or Metropolis? Analyzing the Relationship between Population Size and Language

no code implementations LREC 2020 Amy Rechkemmer, Steven Wilson, Rada Mihalcea

Using a set of over 2 million posts from distinct Twitter users around the country dating back as far as 2014, we ask the following question: is there a difference in how Americans express themselves online depending on whether they reside in an urban or rural area?

MuSE: a Multimodal Dataset of Stressed Emotion

no code implementations LREC 2020 Mimansa Jaiswal, Cristian-Paul Bara, Yuanhang Luo, Mihai Burzo, Rada Mihalcea, Emily Mower Provost

Endowing automated agents with the ability to provide support, entertainment and interaction with human beings requires sensing of the users{'} affective state.

Emotion Classification General Classification

Analyzing the Surprising Variability in Word Embedding Stability Across Languages

1 code implementation EMNLP 2021 Laura Burdick, Jonathan K. Kummerfeld, Rada Mihalcea

Word embeddings are powerful representations that form the foundation of many natural language processing architectures, both in English and in other languages.

Word Embeddings

Compositional Temporal Visual Grounding of Natural Language Event Descriptions

no code implementations4 Dec 2019 Jonathan C. Stroud, Ryan McCaffrey, Rada Mihalcea, Jia Deng, Olga Russakovsky

Temporal grounding entails establishing a correspondence between natural language event descriptions and their visual depictions.

Visual Grounding

Representing Movie Characters in Dialogues

no code implementations CONLL 2019 Mahmoud Azab, Noriyuki Kojima, Jia Deng, Rada Mihalcea

We introduce a new embedding model to represent movie characters and their interactions in a dialogue by encoding in the same representation the language used by these characters as well as information about the other participants in the dialogue.

Question Answering Relation Classification +1

Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines

no code implementations IJCNLP 2019 Mahmoud Azab, Stephane Dadian, Vivi Nastase, Larry An, Rada Mihalcea

We introduce a new dataset consisting of natural language interactions annotated with medical family histories, obtained during interactions with a genetic counselor and through crowdsourcing, following a questionnaire created by experts in the domain.

Relation Extraction

Conversational Transfer Learning for Emotion Recognition

1 code implementation11 Oct 2019 Devamanyu Hazarika, Soujanya Poria, Roger Zimmermann, Rada Mihalcea

We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target).

Emotion Recognition in Conversation Transfer Learning

Towards Automatic Detection of Misinformation in Online Medical Videos

no code implementations4 Sep 2019 Rui Hou, Verónica Pérez-Rosas, Stacy Loeb, Rada Mihalcea

Recent years have witnessed a significant increase in the online sharing of medical information, with videos representing a large fraction of such online sources.

Misinformation

Variational Fusion for Multimodal Sentiment Analysis

no code implementations13 Aug 2019 Navonil Majumder, Soujanya Poria, Gangeshwar Krishnamurthy, Niyati Chhaya, Rada Mihalcea, Alexander Gelbukh

Multimodal fusion is considered a key step in multimodal tasks such as sentiment analysis, emotion detection, question answering, and others.

Multimodal Sentiment Analysis Question Answering

Predicting Human Activities from User-Generated Content

no code implementations ACL 2019 Steven R. Wilson, Rada Mihalcea

The activities we do are linked to our interests, personality, political preferences, and decisions we make about the future.

Sentence Embedding Sentence-Embedding

What Makes a Good Counselor? Learning to Distinguish between High-quality and Low-quality Counseling Conversations

no code implementations ACL 2019 Ver{\'o}nica P{\'e}rez-Rosas, Xinyi Wu, Kenneth Resnicow, Rada Mihalcea

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.

Towards Multimodal Sarcasm Detection (An \_Obviously\_ Perfect Paper)

1 code implementation ACL 2019 Santiago Castro, Devamanyu Hazarika, Ver{\'o}nica P{\'e}rez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria

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.

Sarcasm Detection

Women's Syntactic Resilience and Men's Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing

no code implementations ACL 2019 Aparna Garimella, Carmen Banea, Dirk Hovy, Rada Mihalcea

Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for these differences.

Dependency Parsing Part-Of-Speech Tagging

Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)

1 code implementation5 Jun 2019 Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria

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.

Sarcasm Detection

Box of Lies: Multimodal Deception Detection in Dialogues

no code implementations NAACL 2019 Felix Soldner, Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea

Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection.

Deception Detection General Classification

Look Who's Talking: Inferring Speaker Attributes from Personal Longitudinal Dialog

1 code implementation25 Apr 2019 Charles Welch, Verónica Pérez-Rosas, Jonathan K. Kummerfeld, Rada Mihalcea

We examine a large dialog corpus obtained from the conversation history of a single individual with 104 conversation partners.

14

A Comparative Analysis of Content-based Geolocation in Blogs and Tweets

no code implementations19 Nov 2018 Konstantinos Pappas, Mahmoud Azab, Rada Mihalcea

The geolocation of online information is an essential component in any geospatial application.

DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

2 code implementations1 Nov 2018 Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh, Erik Cambria

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc.

Emotion Classification Emotion Recognition in Conversation +2

Speaker Naming in Movies

no code implementations NAACL 2018 Mahmoud Azab, Mingzhe Wang, Max Smith, Noriyuki Kojima, Jia Deng, Rada Mihalcea

We propose a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework.

Multi-Label Transfer Learning for Multi-Relational Semantic Similarity

no code implementations SEMEVAL 2019 Li Zhang, Steven R. Wilson, Rada Mihalcea

Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e. g., similarity, relatedness, and so on.

Multi-Task Learning Semantic Similarity +1

Factors Influencing the Surprising Instability of Word Embeddings

2 code implementations NAACL 2018 Laura Wendlandt, Jonathan K. Kummerfeld, Rada Mihalcea

Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations.

Word Embeddings

Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity

no code implementations20 Apr 2018 Li Zhang, Steven R. Wilson, Rada Mihalcea

Sentence encoders, which produce sentence embeddings using neural networks, are typically evaluated by how well they transfer to downstream tasks.

Natural Language Understanding Semantic Similarity +4

Measuring Semantic Relations between Human Activities

no code implementations IJCNLP 2017 Steven Wilson, Rada Mihalcea

The things people do in their daily lives can provide valuable insights into their personality, values, and interests.

Semantic Textual Similarity

Identifying Usage Expression Sentences in Consumer Product Reviews

no code implementations IJCNLP 2017 Shibamouli Lahiri, V.G.Vinod Vydiswaran, Rada Mihalcea

The system combines lexical, syntactic, and semantic features in a product-agnostic fashion to yield good classification performance.

Classification General Classification

Demographic-aware word associations

no code implementations EMNLP 2017 Aparna Garimella, Carmen Banea, Rada Mihalcea

Variations of word associations across different groups of people can provide insights into people{'}s psychologies and their world views.

Information Retrieval Keyword Extraction +1

Automatic Detection of Fake News

no code implementations COLING 2018 Verónica Pérez-Rosas, Bennett Kleinberg, Alexandra Lefevre, Rada Mihalcea

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.

Fake News Detection

A Computational Analysis of the Language of Drug Addiction

no code implementations EACL 2017 Carlo Strapparava, Rada Mihalcea

We present a computational analysis of the language of drug users when talking about their drug experiences.

Predicting Counselor Behaviors in Motivational Interviewing Encounters

no code implementations EACL 2017 Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence An, Kathy J. Goggin, Delwyn Catley

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.

Predicting the Industry of Users on Social Media

no code implementations24 Dec 2016 Konstantinos Pappas, Rada Mihalcea

Automatic profiling of social media users is an important task for supporting a multitude of downstream applications.

Ensemble Learning Feature Engineering +2

Stateology: State-Level Interactive Charting of Language, Feelings, and Values

no code implementations20 Dec 2016 Konstantinos Pappas, Steven Wilson, Rada Mihalcea

People's personality and motivations are manifest in their everyday language usage.

Targeted Sentiment to Understand Student Comments

no code implementations COLING 2016 Charles Welch, Rada Mihalcea

We address the task of targeted sentiment as a means of understanding the sentiment that students hold toward courses and instructors, as expressed by students in their comments.

Decision Making Entity Extraction using GAN +1

Zooming in on Gender Differences in Social Media

no code implementations WS 2016 Aparna Garimella, Rada Mihalcea

Men are from Mars and women are from Venus - or so the genre of relationship literature would have us believe.

General Classification Text Classification +1

Identifying Cross-Cultural Differences in Word Usage

no code implementations COLING 2016 Aparna Garimella, Rada Mihalcea, James Pennebaker

Personal writings have inspired researchers in the fields of linguistics and psychology to study the relationship between language and culture to better understand the psychology of people across different cultures.

Building a Dataset for Possessions Identification in Text

no code implementations LREC 2016 Carmen Banea, Xi Chen, Rada Mihalcea

Just as industrialization matured from mass production to customization and personalization, so has the Web migrated from generic content to public disclosures of one{'}s most intimately held thoughts, opinions and beliefs.

Mining Semantic Affordances of Visual Object Categories

no code implementations CVPR 2015 Yu-Wei Chao, Zhan Wang, Rada Mihalcea, Jia Deng

In this paper we introduce the new problem of mining the knowledge of semantic affordance: given an object, determining whether an action can be performed on it.

Collaborative Filtering

Modeling Language Proficiency Using Implicit Feedback

no code implementations LREC 2014 Chris Hokamp, Rada Mihalcea, Peter Schuelke

We describe the results of several experiments with interactive interfaces for native and L2 English students, designed to collect implicit feedback from students as they complete a reading activity.

Reading Comprehension Text Simplification

A Multimodal Dataset for Deception Detection

no code implementations LREC 2014 Ver{\'o}nica P{\'e}rez-Rosas, Rada Mihalcea, Alexis Narvaez, Mihai Burzo

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.

Deception Detection

Authorship Attribution Using Word Network Features

no code implementations12 Nov 2013 Shibamouli Lahiri, Rada Mihalcea

The goal of our paper is to explore properties of these complex networks that are suitable as features for machine-learning-based authorship attribution of documents.

Learning Sentiment Lexicons in Spanish

no code implementations LREC 2012 Ver{\'o}nica P{\'e}rez-Rosas, Carmen Banea, Rada Mihalcea

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.

Opinion Mining Question Answering +4

TextRank: Bringing Order into Texts

1 code implementation Conference 2004 Rada Mihalcea, Paul Tarau

In this paper, we introduce TextRank – a graph-based ranking model for text processing and show how this model can be successfully used in natural language applications.

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