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.
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.
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.
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.
1 code implementation • EMNLP (NLP-COVID19) 2020 • Adam Poliak, Max Fleming, Cash Costello, Kenton Murray, Mahsa Yarmohammadi, Shivani Pandya, Darius Irani, Milind Agarwal, Udit Sharma, Shuo Sun, Nicola Ivanov, Lingxi Shang, Kaushik Srinivasan, Seolhwa Lee, Xu Han, Smisha Agarwal, João Sedoc
We release a dataset of over 2, 100 COVID19 related Frequently asked Question-Answer pairs scraped from over 40 trusted websites.
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.
no code implementations • EACL (WASSA) 2021 • Shabnam Tafreshi, Orphee De Clercq, Valentin Barriere, Sven Buechel, João Sedoc, Alexandra Balahur
This paper presents the results that were obtained from the WASSA 2021 shared task on predicting empathy and emotions.
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.
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.
no code implementations • 16 Dec 2021 • Qi He, João Sedoc, Jordan Rodu
To date, there are no theoretical analyses of the Transformer's ability to capture tree structures.
no code implementations • 16 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.
2 code implementations • 3 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.
no code implementations • 29 Sep 2021 • Seolhwa Lee, Kisu Yang, Chanjun Park, João Sedoc, Heuiseok Lim
To the best of our knowledge, our approach is the first method to apply multi-task learning to the dialogue summarization task.
no code implementations • ACL (GEM) 2021 • Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Aremu Anuoluwapo, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna Clinciu, Dipanjan Das, Kaustubh D. Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Emezue, Varun Gangal, Cristina Garbacea, Tatsunori Hashimoto, Yufang Hou, Yacine Jernite, Harsh Jhamtani, Yangfeng Ji, Shailza Jolly, Mihir Kale, Dhruv Kumar, Faisal Ladhak, Aman Madaan, Mounica Maddela, Khyati Mahajan, Saad Mahamood, Bodhisattwa Prasad Majumder, Pedro Henrique Martins, Angelina McMillan-Major, Simon Mille, Emiel van Miltenburg, Moin Nadeem, Shashi Narayan, Vitaly Nikolaev, Rubungo Andre Niyongabo, Salomey Osei, Ankur Parikh, Laura Perez-Beltrachini, Niranjan Ramesh Rao, Vikas Raunak, Juan Diego Rodriguez, Sashank Santhanam, João Sedoc, Thibault Sellam, Samira Shaikh, Anastasia Shimorina, Marco Antonio Sobrevilla Cabezudo, Hendrik Strobelt, Nishant Subramani, Wei Xu, Diyi Yang, Akhila Yerukola, Jiawei Zhou
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
no code implementations • Findings of the Association for Computational Linguistics 2020 • Huda Khayrallah, João Sedoc
Non-task-oriented dialog models suffer from poor quality and non-diverse responses.
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).
no code implementations • 24 Oct 2020 • Seolhwa Lee, Heuiseok Lim, João Sedoc
These findings demonstrate the feasibility of our protocol to evaluate conversational agents and evaluation sets.
1 code implementation • ACL (GEM) 2021 • Alexandra DeLucia, Aaron Mueller, Xiang Lisa Li, João Sedoc
Narrative generation is an open-ended NLP task in which a model generates a story given a prompt.
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.
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.
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.
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.
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.
1 code implementation • NAACL 2019 • Tianlin Liu, Lyle Ungar, João Sedoc
Distributed representations of sentences have become ubiquitous in natural language processing tasks.
2 code implementations • NAACL 2019 • Reno Kriz, João Sedoc, Marianna Apidianaki, Carolina Zheng, Gaurav Kumar, Eleni Miltsakaki, Chris Callison-Burch
Sentence simplification is the task of rewriting texts so they are easier to understand.
Ranked #4 on
Text Simplification
on Newsela
1 code implementation • 17 Nov 2018 • Tianlin Liu, Lyle Ungar, João Sedoc
Word vectors are at the core of many natural language processing tasks.
no code implementations • COLING (PEOPLES) 2020 • Sven Buechel, João Sedoc, H. Andrew Schwartz, Lyle Ungar
One of the major downsides of Deep Learning is its supposed need for vast amounts of training data.
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.
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.
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.
no code implementations • 2 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.
1 code implementation • 2 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.
1 code implementation • 20 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.