Siva Reddy

Biography

Siva Reddy is an assistant professor at the School of Computer Science and in the Department of Linguistics at McGill University. He completed a postdoc with the Stanford NLP Group in September 2019.

Reddy’s research goal is to enable machines with natural language understanding abilities in order to facilitate applications like question answering and conversational systems. His expertise includes building symbolic (linguistic and induced) and deep learning models for language.

Current Students

Vaibhav Adlakha

PhD - McGill University

Parishad BehnamGhader

Master's Research - McGill University

PhD - McGill University

Collaborating researcher - University of Edinburgh

Jay Gala

Master's Research - McGill University

Co-supervisor :

Collaborating researcher

Gaurav Kamath

PhD - McGill University

Co-supervisor :

Timothy O'Donnell

Imene Kerboua

Collaborating researcher - INSA Lyon, France

PhD - McGill University

Principal supervisor :

PhD - McGill University

Co-supervisor :

Timothy O'Donnell

Aravind Krishnan

Collaborating Alumni - UNIVERSITÄT DES SAARLANDES

Benno Krojer

PhD - McGill University

Zichao Li

PhD - McGill University

Co-supervisor :

Jackie Cheung

Fengyuan Liu

Master's Research - McGill University

Co-supervisor :

Dzmitry Bahdanau

Xing Han Lu

PhD - McGill University

Master's Research - McGill University

Nicholas Meade

PhD - McGill University

Postdoctorate - McGill University

Hanseok Oh

Collaborating researcher

Arkil Patel

PhD - McGill University

Principal supervisor :

Collaborating Alumni

Karolina Ewa Stańczak

Collaborating Alumni - McGill University

Ada Tur

Research Intern - McGill University

PhD - McGill University

Collaborating Alumni - McGill University

How Do We Explain AI and Ensure the Explanation Is True? Faithfulness Measurable Models Tell You How

Blog Posts

October 1, 2024

Andrea Madsen

Siva Reddy

Sarath Chandar

Read the article

Publications

Minimax and Neyman–Pearson Meta-Learning for Outlier Languages

Edoardo Ponti

Rahul Aralikatte

Disha Shrivastava

Anders Sogaard

Model-agnostic meta-learning (MAML) has been recently put forth as a strategy to learn resource-poor languages in a sample-efficient fashion… (see more). Nevertheless, the properties of these languages are often not well represented by those available during training. Hence, we argue that the i.i.d. assumption ingrained in MAML makes it ill-suited for cross-lingual NLP. In fact, under a decision-theoretic framework, MAML can be interpreted as minimising the expected risk across training languages (with a uniform prior), which is known as Bayes criterion. To increase its robustness to outlier languages, we create two variants of MAML based on alternative criteria: Minimax MAML reduces the maximum risk across languages, while Neyman–Pearson MAML constrains the risk in each language to a maximum threshold. Both criteria constitute fully differentiable two-player games. In light of this, we propose a new adaptive optimiser solving for a local approximation to their Nash equilibrium. We evaluate both model variants on two popular NLP tasks, part-of-speech tagging and question answering. We report gains for their average and minimum performance across low-resource languages in zeroand few-shot settings, compared to joint multisource transfer and vanilla MAML. The code for our experiments is available at https:// github.com/rahular/robust-maml.

2021-08-01

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (published)

StereoSet: Measuring stereotypical bias in pretrained language models

Moin Nadeem

Anna Bethke

A stereotype is an over-generalized belief about a particular group of people, e.g., Asians are good at math or African Americans are athlet… (see more)ic. Such beliefs (biases) are known to hurt target groups. Since pretrained language models are trained on large real-world data, they are known to capture stereotypical biases. It is important to quantify to what extent these biases are present in them. Although this is a rapidly growing area of research, existing literature lacks in two important aspects: 1) they mainly evaluate bias of pretrained language models on a small set of artificial sentences, even though these models are trained on natural data 2) current evaluations focus on measuring bias without considering the language modeling ability of a model, which could lead to misleading trust on a model even if it is a poor language model. We address both these problems. We present StereoSet, a large-scale natural English dataset to measure stereotypical biases in four domains: gender, profession, race, and religion. We contrast both stereotypical bias and language modeling ability of popular models like BERT, GPT-2, RoBERTa, and XLnet. We show that these models exhibit strong stereotypical biases. Our data and code are available at https://stereoset.mit.edu.

2021-08-01

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (published)

Modelling Latent Translations for Cross-Lingual Transfer

Edoardo Ponti

Julia Kreutzer

Ivan Vulić

2021-07-23

ArXiv (preprint)

Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle

Yikang Shen

Shawn Tan

Alessandro Sordoni

Aaron Courville

Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization proble… (see more)ms and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with an incremental parser and maintains the conditional probability setting of a standard language model (left-to-right). To train the incremental parser and avoid exposure bias, we also propose a novel dynamic oracle, so that SOM is more robust to wrong parsing decisions. Experiments show that SOM can achieve strong results in language modeling, incremental parsing, and syntactic generalization tests while using fewer parameters than other models.

2021-06-01

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (published)

Understanding by Understanding Not: Modeling Negation in Language Models

Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language mode… (see more)ls often handle negation incorrectly. To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus. By training BERT with the resulting combined objective we reduce the mean top 1 error rate to 4% on the negated LAMA dataset. We also see some improvements on the negated NLI benchmarks.

2021-06-01

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (published)

Abg-CoQA: Clarifying Ambiguity in Conversational Question Answering

Meiqi Guo

Mingda Zhang

Malihe Alikhani

Effective communication is about the dissemination of properly worded meaningful ideas/messages that are comprehensible to both sen… (see more)der and receiver and which ultimately can attract the desired response or feedback. For machines to engage in a conversation, it is therefore essential to enable them to clarify ambiguity and achieve a common ground. We introduce Abg-CoQA, a novel dataset for clarifying ambiguity in Conversational Question Answering systems. Our dataset contains 9k questions with answers where 1k questions are ambiguous, obtained from 4k text passages from five diverse domains. For ambiguous questions, a clarification conversational turn is collected. We evaluate strong language generation models and conversational question answering models on Abg-CoQA. The best-performing system achieves a BLEU-1 score of 12.9% on generating clarification question, which is 27.9 points behind human performance (40.8%); and a F1 score of 40.1% on question answering after clarification, which is 35.1 points behind human performance (75.2%), indicating there is ample room for improvement.

2021-01-01

Conference on Automated Knowledge Base Construction (published)

openreview.net

Explicitly Modeling Syntax in Language Model improves Generalization

Yikang Shen

Shawn Tan

Alessandro Sordoni

Aaron Courville

Syntax is fundamental to our thinking about language. Although neural networks are very successful in many tasks, they do not explicitly mod… (see more)el syntactic structure. Failing to capture the structure of inputs could lead to generalization problems and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with a one-step look-ahead parser and maintains the conditional probability setting of the standard language model. Experiments show that SOM can achieve strong results in language modeling and syntactic generalization tests, while using fewer parameters then other models.

2020-10-21

arXiv.org (preprint)

dblp.uni-trier.de

Words Aren’t Enough, Their Order Matters: On the Robustness of Grounding Visual Referring Expressions

Arjun Reddy Akula

Spandana Gella

Yaser Al-Onaizan

Song-Chun Zhu

Visual referring expression recognition is a challenging task that requires natural language understanding in the context of an image. We cr… (see more)itically examine RefCOCOg, a standard benchmark for this task, using a human study and show that 83.7% of test instances do not require reasoning on linguistic structure, i.e., words are enough to identify the target object, the word order doesn’t matter. To measure the true progress of existing models, we split the test set into two sets, one which requires reasoning on linguistic structure and the other which doesn’t. Additionally, we create an out-of-distribution dataset Ref-Adv by asking crowdworkers to perturb in-domain examples such that the target object changes. Using these datasets, we empirically show that existing methods fail to exploit linguistic structure and are 12% to 23% lower in performance than the established progress for this task. We also propose two methods, one based on contrastive learning and the other based on multi-task learning, to increase the robustness of ViLBERT, the current state-of-the-art model for this task. Our datasets are publicly available at https://github.com/aws/aws-refcocog-adv.

2020-07-01

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (published)

Measuring Systematic Generalization in Neural Proof Generation with Transformers

Nicolas Gontier

Koustuv Sinha

Chris Pal

We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge encoded… (see more) in the form of natural language. We investigate their systematic generalization abilities on a logical reasoning task in natural language, which involves reasoning over relationships between entities grounded in first-order logical proofs. Specifically, we perform soft theorem-proving by leveraging TLMs to generate natural language proofs. We test the generated proofs for logical consistency, along with the accuracy of the final inference. We observe length-generalization issues when evaluated on longer-than-trained sequences. However, we observe TLMs improve their generalization performance after being exposed to longer, exhaustive proofs. In addition, we discover that TLMs are able to generalize better using backward-chaining proofs compared to their forward-chaining counterparts, while they find it easier to generate forward chaining proofs. We observe that models that are not trained to generate proofs are better at generalizing to problems based on longer proofs. This suggests that Transformers have efficient internal reasoning strategies that are harder to interpret. These results highlight the systematic generalization behavior of TLMs in the context of logical reasoning, and we believe this work motivates deeper inspection of their underlying reasoning strategies.

MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining

Zhi Wen

Xing Han Lu

2020-01-01

ClinicalNLP@EMNLP (published)

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… (see more)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 (published)

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… (see more)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 (published)