Search Results for author: João Sedoc

Found 33 papers, 11 papers with code

Measuring the Language of Self-Disclosure across Corpora

no code implementations Findings (ACL) 2022 Ann-Katrin Reuel, Sebastian Peralta, João Sedoc, Garrick Sherman, Lyle Ungar

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

no code implementations WASSA (ACL) 2022 Valentin Barriere, Shabnam Tafreshi, João Sedoc, Sawsan Alqahtani

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

no code implementations insights (ACL) 2022 Pedro Rodriguez, Phu Mon Htut, John Lalor, João Sedoc

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.

Natural Language Inference Question Answering

Using the Poly-encoder for a COVID-19 Question Answering System

no code implementations EMNLP (NLP-COVID19) 2020 Seolhwa Lee, João Sedoc

To combat misinformation regarding COVID- 19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19.

Misinformation Question Answering

Multi-Emotion Classification for Song Lyrics

no code implementations EACL (WASSA) 2021 Darren Edmonds, João Sedoc

Song lyrics convey a multitude of emotions to the listener and powerfully portray the emotional state of the writer or singer.

Classification Emotion Classification

Topic Modeling for Maternal Health Using Reddit

no code implementations EACL (Louhi) 2021 Shuang Gao, Shivani Pandya, Smisha Agarwal, João Sedoc

This paper applies topic modeling to understand maternal health topics, concerns, and questions expressed in online communities on social networking sites.

Knowledge Distillation

Item Response Theory for Efficient Human Evaluation of Chatbots

no code implementations EMNLP (Eval4NLP) 2020 João Sedoc, Lyle Ungar

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

no code implementations16 Dec 2021 Qi He, João Sedoc, Jordan Rodu

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

no code implementations16 Dec 2021 Prasanna Parasurama, João Sedoc

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

2 code implementations3 Nov 2021 Chen Zhang, João Sedoc, Luis Fernando D'Haro, Rafael Banchs, Alexander Rudnicky

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.

Chatbot Dialogue Evaluation

Measuring the `I don't know' Problem through the Lens of Gricean Quantity

no code implementations NAACL 2021 Huda Khayrallah, João Sedoc

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

no code implementations24 Oct 2020 Seolhwa Lee, Heuiseok Lim, João Sedoc

These findings demonstrate the feasibility of our protocol to evaluate conversational agents and evaluation sets.

Experimental Design

COD3S: Diverse Generation with Discrete Semantic Signatures

1 code implementation EMNLP 2020 Nathaniel Weir, João Sedoc, Benjamin Van Durme

We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models.

Semantic Textual Similarity

Incremental Neural Coreference Resolution in Constant Memory

no code implementations EMNLP 2020 Patrick Xia, João Sedoc, Benjamin Van Durme

We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components.

Coreference Resolution

Learning Word Ratings for Empathy and Distress from Document-Level User Responses

no code implementations LREC 2020 João Sedoc, Sven Buechel, Yehonathan Nachmany, Anneke Buffone, Lyle Ungar

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.

Emotion Recognition

Conceptor Debiasing of Word Representations Evaluated on WEAT

no code implementations WS 2019 Saket Karve, Lyle Ungar, João Sedoc

Bias in word embeddings such as Word2Vec has been widely investigated, and many efforts made to remove such bias.

Word Embeddings

Comparison of Diverse Decoding Methods from Conditional Language Models

1 code implementation ACL 2019 Daphne Ippolito, Reno Kriz, Maria Kustikova, João Sedoc, Chris Callison-Burch

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.

Modeling Empathy and Distress in Reaction to News Stories

1 code implementation EMNLP 2018 Sven Buechel, Anneke Buffone, Barry Slaff, Lyle Ungar, João Sedoc

Computational detection and understanding of empathy is an important factor in advancing human-computer interaction.

Multiscale Hidden Markov Models For Covariance Prediction

no code implementations ICLR 2018 João Sedoc, Jordan Rodu, Dean Foster, Lyle Ungar

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

no code implementations ICLR 2018 João Sedoc, Dean Foster, Lyle Ungar

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

no code implementations2 Aug 2017 Sajal Choudhary, Prerna Srivastava, Lyle Ungar, João Sedoc

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

1 code implementation2 Aug 2017 Grishma Jena, Mansi Vashisht, Abheek Basu, Lyle Ungar, João Sedoc

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

1 code implementation20 Jan 2016 João Sedoc, Jean Gallier, Lyle Ungar, Dean Foster

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.

Word Similarity

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