Alessandro Sordoni

Membre industriel principal

Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle

Chercheur scientifique, Microsoft Research Montréal

Sujets de recherche

Grands modèles de langage (LLM)

Raisonnement

Traitement du langage naturel

Biographie

Je suis chercheur principal à Microsoft Research Montréal. J'ai obtenu un doctorat de l'Université de Montréal sous la direction de Jian-Yun Nie, en étudiant comment représenter efficacement les documents et les requêtes pour la recherche d'information. Présentement, je m’intéresse à l'étude de l'efficacité de l'apprentissage et de la généralisation systématique dans les grands modèles actuels d'apprentissage profond. Mes intérêts s'étendent à l'apprentissage non supervisé et à l'apprentissage à petite échelle, en particulier dans le domaine du langage naturel.

Étudiants actuels

Zhan Su

Collaborateur·rice alumni - University of Copenhagen

Publications

Towards Policy-Guided Conversational Recommendation with Dialogue Acts

Paul Crook

Y-Lan Boureau

J. Weston

Akbar Karimi

Leonardo Rossi

Andrea Prati

Wenqiang Lei

Xiangnan He

Qingyun Yisong Miao

Richang Wu

Min-Yen Hong

Kan Tat-Seng

Raymond Li

Samira Ebrahimi Kahou

Hannes Schulz

Zujie Liang

Huang Hu

Can Xu

Jian Miao

Lizi Liao … (voir 47 de plus)

Ryuichi Takanobu

Yunshan Ma

Xun Yang

Wenchang Ma

Minlie Huang

Minghao Tu

Iulian Serban

Aaron C. Courville

David Silver

Julian Schrittwieser

K. Simonyan

Ioannis Antonoglou

Aja Huang

A. Guez

Hanlin Zhu

O. Vinyals

Igor Babuschkin

Junyoung Chung

M. Mathieu

Max Jaderberg

Wojciech M. Czar-725 necki

A. Dudzik

Petko Georgiev

Richard Powell

T. Ewalds

Dan Horgan

M. Kroiss

Ivo Danihelka

J. Agapiou

Junhyuk Oh

Valentin Dalibard

David Choi

L. Sifre

Yury Sulsky

Sasha Vezhnevets

James Molloy

Trevor Cai

D. Budden

T. Paine

Caglar Gulçehre

Ziyu Wang

Tobias Pfaff

Tobias Pohlen

2021-12-31

(publié)

www.semanticscholar.org

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

Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization proble… (voir plus)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.

2021-05-31

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

doi.org

arxiv.org

Understanding by Understanding Not: Modeling Negation in Language Models

R Devon Hjelm

Negation is a core construction in natural language. Despite being very successful on many tasks, state-of-the-art pre-trained language mode… (voir plus)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.

2021-05-31

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

doi.org

arxiv.org

What Makes Machine Reading Comprehension Questions Difﬁcult? Investigating Variation in Passage Sources and Question Types

Susan Bartlett

Grzegorz Kondrak

Max Bartolo

Alastair Roberts

Johannes Welbl

Steven Bird

Ewan Klein

Edward Loper

Samuel R. Bowman

George Dahl. 2021

What

Chao Pang

Junyuan Shang

Jiaxiang Liu

Xuyi Chen

Yanbin Zhao

Yuxiang Lu

Weixin Liu

Zhi-901 hua Wu

Weibao Gong … (voir 21 de plus)

Jianzhong Liang

Zhizhou Shang

Peng Sun

Ouyang Xuan

Dianhai

Houwen Tian

Hua Wu

Haifeng Wang

Adam Trischler

Tong Wang

Xingdi Yuan

Justin Har-908

Alessandro Sordoni

Philip Bachman

Adina Williams

Nikita Nangia

Zhilin Yang

Peng Qi

Saizheng Zhang

Yoshua Bengio

ing. In

For a natural language understanding bench-001 mark to be useful in research, it has to con-002 sist of examples that are diverse and difﬁ… (voir plus)-003 cult enough to discriminate among current and 004 near-future state-of-the-art systems. However, 005 we do not yet know how best to select pas-006 sages to collect a variety of challenging exam-007 ples. In this study, we crowdsource multiple-008 choice reading comprehension questions for 009 passages taken from seven qualitatively dis-010 tinct sources, analyzing what attributes of pas-011 sages contribute to the difﬁculty and question 012 types of the collected examples. To our sur-013 prise, we ﬁnd that passage source, length, and 014 readability measures do not signiﬁcantly affect 015 question difﬁculty. Through our manual anno-016 tation of seven reasoning types, we observe 017 several trends between passage sources and 018 reasoning types, e.g., logical reasoning is more 019 often required in questions written for techni-020 cal passages. These results suggest that when 021 creating a new benchmark dataset, selecting a 022 diverse set of passages can help ensure a di-023 verse range of question types, but that passage 024 difﬁculty need not be a priority. 025

2020-12-31

(publié)

www.semanticscholar.org

Recursive Top-Down Production for Sentence Generation with Latent Trees

Timothy J. O'Donnell

We model the recursive production property of context-free grammars for natural and synthetic languages. To this end, we present a dynamic p… (voir plus)rogramming algorithm that marginalises over latent binary tree structures with

2020-10-31

Findings of the Association for Computational Linguistics: EMNLP 2020 (publié)

doi.org

arxiv.org

Explicitly Modeling Syntax in Language Model improves Generalization

Syntax is fundamental to our thinking about language. Although neural networks are very successful in many tasks, they do not explicitly mod… (voir plus)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.

2020-10-20

arXiv.org (prépublication)

Ordered Memory

Stack-augmented recurrent neural networks (RNNs) have been of interest to the deep learning community for some time. However, the difficulty… (voir plus) of training memory models remains a problem obstructing the widespread use of such models. In this paper, we propose the Ordered Memory architecture. Inspired by Ordered Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and use its cumulative probability to control the writing and erasing operation of the memory. We also introduce a new Gated Recursive Cell to compose lower-level representations into higher-level representation. We demonstrate that our model achieves strong performance on the logical inference task (Bowman et al., 2015)and the ListOps (Nangia and Bowman, 2018) task. We can also interpret the model to retrieve the induced tree structure, and find that these induced structures align with the ground truth. Finally, we evaluate our model on the Stanford SentimentTreebank tasks (Socher et al., 2013), and find that it performs comparatively with the state-of-the-art methods in the literature.

2019-10-28

ArXiv (prépublication)

doi.org

arxiv.org

Brief Report: Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Yikeng Shen

Shawn Tan

Alessandro Sordoni

Aaron Courville

2019-05-05

(publié)

www.semanticscholar.org

An Empirical Study of Example Forgetting During Deep Neural Network Learning

Mariya Toneva

Alessandro Sordoni

Remi Tachet des Combes

Adam Trischler

Yoshua Bengio

Geoffrey J. Gordon

Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single class… (voir plus)ification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a `forgetting event' to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set's (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.

2018-12-31

ICLR.cc/2019/Conference (poster)

doi.org

openreview.net

Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger c… (voir plus)onstituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such an inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.

2018-12-31

ICLR.cc/2019/Conference (présentation orale)

openreview.net

Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data

Amjad Almahairi

Sai Rajeswar

Alessandro Sordoni

Philip Bachman

Aaron Courville

Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by red… (voir plus)ucing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-to-one. This assumption renders the model ineffective for tasks requiring flexible, many-to-many mappings. We propose a new model, called Augmented CycleGAN, which learns many-to-many mappings between domains. We examine Augmented CycleGAN qualitatively and quantitatively on several image datasets.

2018-07-02

Proceedings of the 35th International Conference on Machine Learning (publié)

proceedings.mlr.press

Focused Hierarchical RNNs for Conditional Sequence Processing

Nan Rosemary Ke

Adam Trischler

Recurrent Neural Networks (RNNs) with attention mechanisms have obtained state-of-the-art results for many sequence processing tasks. Most o… (voir plus)f these models use a simple form of encoder with attention that looks over the entire sequence and assigns a weight to each token independently. We present a mechanism for focusing RNN encoders for sequence modelling tasks which allows them to attend to key parts of the input as needed. We formulate this using a multi-layer conditional sequence encoder that reads in one token at a time and makes a discrete decision on whether the token is relevant to the context or question being asked. The discrete gating mechanism takes in the context embedding and the current hidden state as inputs and controls information flow into the layer above. We train it using policy gradient methods. We evaluate this method on several types of tasks with different attributes. First, we evaluate the method on synthetic tasks which allow us to evaluate the model for its generalization ability and probe the behavior of the gates in more controlled settings. We then evaluate this approach on large scale Question Answering tasks including the challenging MS MARCO and SearchQA tasks. Our models shows consistent improvements for both tasks over prior work and our baselines. It has also shown to generalize significantly better on synthetic tasks as compared to the baselines.

2018-07-02

Proceedings of the 35th International Conference on Machine Learning (publié)

doi.org

proceedings.mlr.press

TRAIL : IA responsable pour les professionnels et les leaders

Fondateur en résidence Mila Ventures

Avantage IA : productivité dans la fonction publique

Alessandro Sordoni

Biographie

Étudiants actuels

Publications

TRAIL : IA responsable pour les professionnels et les leaders

Fondateur en résidence Mila Ventures

Avantage IA : productivité dans la fonction publique

Mots-clés populaires:

Alessandro Sordoni

Biographie

Étudiants actuels

Publications