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Found 33 papers, 11 papers with code
Measuring the Language of Self-Disclosure across Corpora
Being able to reliably estimate self-disclosure – a key component of friendship and intimacy – from language is important for many psychology studies.
WASSA 2022 Shared Task: Predicting Empathy, Emotion and Personality in Reaction to News Stories
This paper presents the results that were obtained from WASSA 2022 shared task on predicting empathy, emotion, and personality in reaction to news stories.
Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory
In natural language processing, multi-dataset benchmarks for common tasks (e. g., SuperGLUE for natural language inference and MRQA for question answering) have risen in importance.
Using the Poly-encoder for a COVID-19 Question Answering System
To combat misinformation regarding COVID- 19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19.
Collecting Verified COVID-19 Question Answer Pairs
We release a dataset of over 2, 100 COVID19 related Frequently asked Question-Answer pairs scraped from over 40 trusted websites.
Multi-Emotion Classification for Song Lyrics
Song lyrics convey a multitude of emotions to the listener and powerfully portray the emotional state of the writer or singer.
WASSA 2021 Shared Task: Predicting Empathy and Emotion in Reaction to News Stories
This paper presents the results that were obtained from the WASSA 2021 shared task on predicting empathy and emotions.
Topic Modeling for Maternal Health Using Reddit
This paper applies topic modeling to understand maternal health topics, concerns, and questions expressed in online communities on social networking sites.
Item Response Theory for Efficient Human Evaluation of Chatbots
Conversational agent quality is currently assessed using human evaluation, and often requires an exorbitant number of comparisons to achieve statistical significance.
Trees in transformers: a theoretical analysis of the Transformer's ability to represent trees
To date, there are no theoretical analyses of the Transformer's ability to capture tree structures.
Gendered Language in Resumes and its Implications for Algorithmic Bias in Hiring
Using a corpus of 709k resumes from IT firms, we train a series of models to classify the gender of the applicant, thereby measuring the extent of gendered information encoded in resumes.
Automatic Evaluation and Moderation of Open-domain Dialogue Systems
The development of Open-Domain Dialogue Systems (ODS)is a trending topic due to the large number of research challenges, large societal and business impact, and advances in the underlying technology.
Who speaks like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning
To the best of our knowledge, our approach is the first method to apply multi-task learning to the dialogue summarization task.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Data-to-Text Generation
on WebNLG ru
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multiple Reference Training
Non-task-oriented dialog models suffer from poor quality and non-diverse responses.
Measuring the `I don't know' Problem through the Lens of Gricean Quantity
We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice's Maxims of Conversation (1975).
An Evaluation Protocol for Generative Conversational Systems
These findings demonstrate the feasibility of our protocol to evaluate conversational agents and evaluation sets.
Decoding Methods for Neural Narrative Generation
Narrative generation is an open-ended NLP task in which a model generates a story given a prompt.
COD3S: Diverse Generation with Discrete Semantic Signatures
We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models.
Incremental Neural Coreference Resolution in Constant Memory
We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components.
Learning Word Ratings for Empathy and Distress from Document-Level User Responses
The underlying problem of learning word ratings from higher-level supervision has to date only been addressed in an ad hoc fashion and has not used deep learning methods.
Conceptor Debiasing of Word Representations Evaluated on WEAT
Bias in word embeddings such as Word2Vec has been widely investigated, and many efforts made to remove such bias.
Comparison of Diverse Decoding Methods from Conditional Language Models
While conditional language models have greatly improved in their ability to output high-quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences.
Continual Learning for Sentence Representations Using Conceptors
Distributed representations of sentences have become ubiquitous in natural language processing tasks.
Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification
Sentence simplification is the task of rewriting texts so they are easier to understand.
Ranked #4 on
Text Simplification
on Newsela
Unsupervised Post-processing of Word Vectors via Conceptor Negation
Word vectors are at the core of many natural language processing tasks.
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations
One of the major downsides of Deep Learning is its supposed need for vast amounts of training data.
Modeling Empathy and Distress in Reaction to News Stories
Computational detection and understanding of empathy is an important factor in advancing human-computer interaction.
Multiscale Hidden Markov Models For Covariance Prediction
This paper presents a novel variant of hierarchical hidden Markov models (HMMs), the multiscale hidden Markov model (MSHMM), and an associated spectral estimation and prediction scheme that is consistent, finds global optima, and is computationally efficient.
Neural Tree Transducers for Tree to Tree Learning
We introduce a novel approach to tree-to-tree learning, the neural tree transducer (NTT), a top-down depth first context-sensitive tree decoder, which is paired with recursive neural encoders.
Domain Aware Neural Dialog System
We investigate the task of building a domain aware chat system which generates intelligent responses in a conversation comprising of different domains.
Enterprise to Computer: Star Trek chatbot
In this work, we propose a design for a chatbot that captures the "style" of Star Trek by incorporating references from the show along with peculiar tones of the fictional characters therein.
Semantic Word Clusters Using Signed Normalized Graph Cuts
Vector space representations of words capture many aspects of word similarity, but such methods tend to make vector spaces in which antonyms (as well as synonyms) are close to each other.
