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Found 15 papers, 4 papers with code
Automatic Detection of Fake News
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.
Look Who's Talking: Inferring Speaker Attributes from Personal Longitudinal Dialog
We examine a large dialog corpus obtained from the conversation history of a single individual with 104 conversation partners.
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)
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.
Towards Automatic Detection of Misinformation in Online Medical Videos
Recent years have witnessed a significant increase in the online sharing of medical information, with videos representing a large fraction of such online sources.
Expressive Interviewing: A Conversational System for Coping with COVID-19
The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability.
Compositional Demographic Word Embeddings
Word embeddings are usually derived from corpora containing text from many individuals, thus leading to general purpose representations rather than individually personalized representations.
Biased TextRank: Unsupervised Graph-Based Content Extraction
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."
Exploring the Value of Personalized Word Embeddings
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.
Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News
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.
Exploring Self-Identified Counseling Expertise in Online Support Forums
A growing number of people engage in online health forums, making it important to understand the quality of the advice they receive.
Matching Tweets With Applicable Fact-Checks Across Languages
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.
Query Rewriting for Effective Misinformation Discovery
We propose a novel system to help fact-checkers formulate search queries for known misinformation claims and effectively search across multiple social media platforms.
VERVE: Template-based ReflectiVE Rewriting for MotiVational IntErviewing
We introduce VERVE, a template-based rewriting system with paraphrase-augmented training and adaptive template updating.
Counseling-Style Reflection Generation Using Generative Pretrained Transformers with Augmented Context
We introduce a counseling dialogue system that seeks to assist counselors while they are learning and refining their counseling skills.
Evaluating Automatic Speech Recognition Quality and Its Impact on Counselor Utterance Coding
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
Automatic Speech Recognition (ASR)
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