Siva Reddy

Biographie

Siva Reddy est professeur adjoint en informatique et linguistique à l’Université McGill. Ses travaux portent sur les algorithmes qui permettent aux ordinateurs de comprendre et de traiter les langues humaines. Il a fait ses études postdoctorales avec le Stanford NLP Group. Son expertise inclut la construction de symboliques linguistiques et induites et de modèles d’apprentissage profond pour le langage.

Étudiants actuels

Vaibhav Adlakha

Doctorat - McGill

Parishad BehnamGhader

Maîtrise recherche - McGill

Doctorat - McGill

Collaborateur·rice de recherche

Gaurav Kamath

Doctorat - McGill

Amirhossein Kazemnejad

Maîtrise recherche - McGill

Github

Aditi Khandelwal

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - McGill

Stagiaire de recherche - UNIVERSITÄT DES SAARLANDES

Benno Krojer

Doctorat - McGill

andreas.madsen@mila.quebec

Zichao Li

Doctorat - McGill

Co-superviseur⋅e :

Jackie Cheung

Xing Han Lu

Doctorat - McGill

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Sarath Chandar

Doctorat - McGill

Postdoctorat - McGill

Arkil Patel

Doctorat - McGill

Superviseur⋅e principal⋅e :

Nooshin Soheililangroodi

Stagiaire de recherche - McGill

Karolina Ewa Stańczak

Postdoctorat - McGill

Github

Ada Tur Tur

Stagiaire de recherche - McGill

Github

Comment expliquer l’IA et s’assurer que cette explication est vraie? Les modèles mesurables de fidélité vous indiquent comment y parvenir

Ivan Vulić

Collaborateur·rice de recherche - Cambridge University

Alejandra Zambrano

Stagiaire de recherche - McGill

Billets de blogue

1 octobre 2024

par

Andrea Madsen

Siva Reddy

Sarath Chandar

Lire l'article

Publications

You could have said that instead: Improving Chatbots with Natural Language Feedback

Makesh Narsimhan Sreedhar

Kun Ni

The ubiquitous nature of dialogue systems and their interaction with users generate an enormous amount of data. Can we improve chatbots usin… (voir plus)g this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample. In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation. The generator’s goal is to convert the feedback into a response that answers the user’s previous utterance and to fool the discriminator which distinguishes feedback from natural responses. We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94%to 75.96% in ranking correct responses on the PERSONACHATdataset, a large improvement given that the original model is already trained on 131k samples.

2020-01-01

Conference on Empirical Methods in Natural Language Processing (publié)

doi.org

arxiv.org

CoQA: A Conversational Question Answering Challenge

Danqi Chen

Christopher D. Manning

Humans gather information through conversations involving a series of interconnected questions and answers. For machines to assist in inform… (voir plus)ation gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets (e.g., coreference and pragmatic reasoning). We evaluate strong dialogue and reading comprehension models on CoQA. The best system obtains an F1 score of 65.4%, which is 23.4 points behind human performance (88.8%), indicating that there is ample room for improvement. We present CoQA as a challenge to the community at https://stanfordnlp.github.io/coqa.

2019-11-01

Transactions of the Association for Computational Linguistics (publié)

doi.org

arxiv.org

Building a Neural Semantic Parser from a Domain Ontology

Jianpeng Cheng

Mirella Lapata

Semantic parsing is the task of converting natural language utterances into machine interpretable meaning representations which can be execu… (voir plus)ted against a real-world environment such as a database. Scaling semantic parsing to arbitrary domains faces two interrelated challenges: obtaining broad coverage training data effectively and cheaply; and developing a model that generalizes to compositional utterances and complex intentions. We address these challenges with a framework which allows to elicit training data from a domain ontology and bootstrap a neural parser which recursively builds derivations of logical forms. In our framework meaning representations are described by sequences of natural language templates, where each template corresponds to a decomposed fragment of the underlying meaning representation. Although artificial, templates can be understood and paraphrased by humans to create natural utterances, resulting in parallel triples of utterances, meaning representations, and their decompositions. These allow us to train a neural semantic parser which learns to compose rules in deriving meaning representations. We crowdsource training data on six domains, covering both single-turn utterances which exhibit rich compositionality, and sequential utterances where a complex task is procedurally performed in steps. We then develop neural semantic parsers which perform such compositional tasks. In general, our approach allows to deploy neural semantic parsers quickly and cheaply from a given domain ontology.

2018-12-25

ArXiv (prépublication)

arxiv.org

Learning Typed Entailment Graphs with Global Soft Constraints

Mohammad Javad Hosseini

Nathanael Chambers