Search Results for author:
Found 57 papers, 10 papers with code
Automatic Detection and Prediction of Psychiatric Hospitalizations From Social Media Posts
We address the problem of predicting psychiatric hospitalizations using linguistic features drawn from social media posts.
Detection of Mental Health from Reddit via Deep Contextualized Representations
We address the problem of automatic detection of psychiatric disorders from the linguistic content of social media posts.
A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter
Most efforts at identifying abusive speech online rely on public corpora that have been scraped from websites using keyword-based queries or released by site or platform owners for research purposes.
CHoRaL: Collecting Humor Reaction Labels from Millions of Social Media Users
Humor detection has gained attention in recent years due to the desire to understand user-generated content with figurative language.
Personalized Attacks of Social Engineering in Multi-turn Conversations -- LLM Agents for Simulation and Detection
The rapid advancement of conversational agents, particularly chatbots powered by Large Language Models (LLMs), poses a significant risk of social engineering (SE) attacks on social media platforms.
SYNTHEMPATHY: A Scalable Empathy Corpus Generated Using LLMs Without Any Crowdsourcing
Previous research has shown that humans are more receptive towards language models that that exhibit empathetic behavior.
Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues
In this paper, we introduce the Akan Conversation Emotion (ACE) dataset, the first multimodal emotion dialogue dataset for an African language, addressing the significant lack of resources for low-resource languages in emotion recognition research.
Can Open-source LLMs Enhance Data Synthesis for Toxic Detection?: An Experimental Study
This study explores the potential of open-source LLMs for harmful data synthesis, utilizing prompt engineering and fine-tuning techniques to enhance data quality and diversity.
PAPILLON: Privacy Preservation from Internet-based and Local Language Model Ensembles
While open-source models, hosted locally on the user's machine, alleviate some concerns, models that users can host locally are often less capable than proprietary frontier models.
From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues
Self-anthropomorphism in robots manifests itself through their display of human-like characteristics in dialogue, such as expressing preferences and emotions.
EmoKnob: Enhance Voice Cloning with Fine-Grained Emotion Control
While recent advances in Text-to-Speech (TTS) technology produce natural and expressive speech, they lack the option for users to select emotion and control intensity.
PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent
Propaganda plays a critical role in shaping public opinion and fueling disinformation.
CREAM: Comparison-Based Reference-Free ELO-Ranked Automatic Evaluation for Meeting Summarization
Large Language Models (LLMs) have spurred interest in automatic evaluation methods for summarization, offering a faster, more cost-effective alternative to human evaluation.
NovAScore: A New Automated Metric for Evaluating Document Level Novelty
The rapid expansion of online content has intensified the issue of information redundancy, underscoring the need for solutions that can identify genuinely new information.
Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances
Emotion recognition in speech is a challenging multimodal task that requires understanding both verbal content and vocal nuances.
Ranked #3 on
Emotion Recognition in Conversation
on IEMOCAP
Emotion Recognition in Conversation
Natural Language Understanding
EDEN: Empathetic Dialogues for English learning
Student passion and perseverance, or grit, has been associated with language learning success.
Defending Against Social Engineering Attacks in the Age of LLMs
The proliferation of Large Language Models (LLMs) poses challenges in detecting and mitigating digital deception, as these models can emulate human conversational patterns and facilitate chat-based social engineering (CSE) attacks.
Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes
The results exhibit a strong correlation for reference notes across different datasets, indicating that format mismatch (i. e., discrepancies in word distribution) is not the main cause of performance decline on out-of-domain data.
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension
Machine Reading Comprehension (MRC) poses a significant challenge in the field of Natural Language Processing (NLP).
Using Adaptive Empathetic Responses for Teaching English
Existing English-teaching chatbots rarely incorporate empathy explicitly in their feedback, but empathetic feedback could help keep students engaged and reduce learner anxiety.
QASE Enhanced PLMs: Improved Control in Text Generation for MRC
To address the challenges of out-of-control generation in generative models for machine reading comprehension (MRC), we introduce the Question-Attended Span Extraction (QASE) module.
Measuring Entrainment in Spontaneous Code-switched Speech
It is well-known that speakers who entrain to one another have more successful conversations than those who do not.
Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement
Speech emotion recognition (SER) often experiences reduced performance due to background noise.
MultiPA: A Multi-task Speech Pronunciation Assessment Model for Open Response Scenarios
Pronunciation assessment models designed for open response scenarios enable users to practice language skills in a manner similar to real-life communication.
Multimodal Multi-loss Fusion Network for Sentiment Analysis
This paper investigates the optimal selection and fusion of feature encoders across multiple modalities and combines these in one neural network to improve sentiment detection.
Ranked #1 on
Multimodal Sentiment Analysis
on CMU-MOSI
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines
These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to generate responses that are more closely aligned with the developer's expectations and intent.
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence
In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society.
A Survey on Open Information Extraction from Rule-based Model to Large Language Model
Open Information Extraction (OpenIE) represents a crucial NLP task aimed at deriving structured information from unstructured text, unrestricted by relation type or domain.
Understanding How People Rate Their Conversations
In this work, we conduct a study to better understand how people rate their interactions with conversational agents.
``Talk to me with left, right, and angles'': Lexical entrainment in spoken Hebrew dialogue
It has been well-documented for several languages that human interlocutors tend to adapt their linguistic productions to become more similar to each other.
Acoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies
We analyzed the acoustic-prosodic and linguistic characteristics of language trusted and mistrusted by raters and compared these to characteristics of actual truthful and deceptive language to understand how perception aligns with reality.
Part of speech tagging for code switched data
We address the problem of Part of Speech tagging (POS) in the context of linguistic code switching (CS).
Crowdsourced Hedge Term Disambiguation
We address the issue of acquiring quality annotations of hedging words and phrases, linguistic phenomenona in which words, sounds, or other constructions are used to express ambiguity or uncertainty.
Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data.
SpatialNet: A Declarative Resource for Spatial Relations
This paper introduces SpatialNet, a novel resource which links linguistic expressions to actual spatial configurations.
Joint Part-of-Speech and Language ID Tagging for Code-Switched Data
Code-switching is the fluent alternation between two or more languages in conversation between bilinguals.
Using Hedge Detection to Improve Committed Belief Tagging
We describe a novel method for identifying hedge terms using a set of manually constructed rules.
Linguistic Cues to Deception and Perceived Deception in Interview Dialogues
We explore deception detection in interview dialogues.
Comparing Approaches for Automatic Question Identification
Collecting spontaneous speech corpora that are open-ended, yet topically constrained, is increasingly popular for research in spoken dialogue systems and speaker state, inter alia.
Crowdsourcing Universal Part-Of-Speech Tags for Code-Switching
We split the annotation task into three subtasks: one in which a subset of tokens are labeled automatically, one in which questions are specifically designed to disambiguate a subset of high frequency words, and a more general cascaded approach for the remaining data in which questions are displayed to the worker following a decision tree structure.
Incrementally Learning a Dependency Parser to Support Language Documentation in Field Linguistics
We have created a new parallel corpus of descriptions of spatial relations and motion events, based on pictures and video clips used by field linguists for elicitation of language from native speaker informants.
Teenage and adult speech in school context: building and processing a corpus of European Portuguese
We present a corpus of European Portuguese spoken by teenagers and adults in school context, CPE-FACES, with an overview of the differential characteristics of high school oral presentations and the challenges this data poses to automatic speech processing.
