379 citations · 780 across the 76 of their papers we have counts for
15 papers · 1 filter
Improving Zero Shot Learning Baselines with Commonsense Knowledge
Abhinaba Roy, Deepanway Ghosal, Erik Cambria +3
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…
Recognizing Emotion Cause in Conversations
Soujanya Poria, Navonil Majumder, Devamanyu Hazarika +9
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 str…
Exploring the Value of Personalized Word Embeddings
Charles Welch, Jonathan K. Kummerfeld, Verónica Pérez-Rosas +1
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 pers…
Biased TextRank: Unsupervised Graph-Based Content Extraction
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 pr…
Deep Learning for Text Style Transfer: A Survey
Di Jin, Zhijing Jin, Zhiting Hu +2
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 man…
Compositional Demographic Word Embeddings
Charles Welch, Jonathan K. Kummerfeld, Verónica Pérez-Rosas +1
Word embeddings are usually derived from corpora containing text from many individuals, thus leading to general purpose representations rather than individually personalized repres…
Utterance-level Dialogue Understanding: An Empirical Study
Deepanway Ghosal, Navonil Majumder, Rada Mihalcea +1
The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding. Complete utterance-level understanding often requires…
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations
Deepanway Ghosal, Navonil Majumder, Alexander Gelbukh +2
In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates differe…
MIME: MIMicking Emotions for Empathetic Response Generation
Navonil Majumder, Pengfei Hong, Shanshan Peng +5
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. We argu…
Improving Low Compute Language Modeling with In-Domain Embedding Initialisation
Charles Welch, Rada Mihalcea, Jonathan K. Kummerfeld
Many NLP applications, such as biomedical data and technical support, have 10-100 million tokens of in-domain data and limited computational resources for learning from it. How sho…
Quantifying the Effects of COVID-19 on Mental Health Support Forums
Laura Biester, Katie Matton, Janarthanan Rajendran +2
The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health. Understanding its impact can inform strategie…
Expressive Interviewing: A Conversational System for Coping with COVID-19
Charles Welch, Allison Lahnala, Verónica Pérez-Rosas +6
The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprec…
KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis
Deepanway Ghosal, Devamanyu Hazarika, Abhinaba Roy +3
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 se…
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder +1
Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago. It has widespread commercial applications in various domains like mar…
Analyzing the Surprising Variability in Word Embedding Stability Across Languages
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. To gain further ins…