Search Results for author: Adam Trischler

Found 52 papers, 28 papers with code

Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications

no code implementations13 May 2022 Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler, Hal Daumé III, Kaheer Suleman, Alexandra Olteanu

There are many ways to express similar things in text, which makes evaluating natural language generation (NLG) systems difficult.

Text Generation

Modeling Event Plausibility with Consistent Conceptual Abstraction

1 code implementation NAACL 2021 Ian Porada, Kaheer Suleman, Adam Trischler, Jackie Chi Kit Cheung

Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events.

Common Sense Reasoning

BUTLER: Building Understanding in TextWorld via Language for Embodied Reasoning

no code implementations ICLR 2021 Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Cote, Yonatan Bisk, Adam Trischler, Matthew Hausknecht

ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions.

Scene Understanding

On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT

1 code implementation Joint Conference on Lexical and Computational Semantics 2020 Abhilasha Ravichander, Eduard Hovy, Kaheer Suleman, Adam Trischler, Jackie Chi Kit Cheung

In particular, we demonstrate through a simple consistency probe that the ability to correctly retrieve hypernyms in cloze tasks, as used in prior work, does not correspond to systematic knowledge in BERT.

An Analysis of Dataset Overlap on Winograd-Style Tasks

no code implementations COLING 2020 Ali Emami, Adam Trischler, Kaheer Suleman, Jackie Chi Kit Cheung

The Winograd Schema Challenge (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR).

Common Sense Reasoning

ALFWorld: Aligning Text and Embodied Environments for Interactive Learning

1 code implementation8 Oct 2020 Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, Matthew Hausknecht

ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions.

Natural Language Visual Grounding Scene Understanding

An Empirical Study on Neural Keyphrase Generation

no code implementations NAACL 2021 Rui Meng, Xingdi Yuan, Tong Wang, Sanqiang Zhao, Adam Trischler, Daqing He

Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them.

Keyphrase Generation

Exploring and Predicting Transferability across NLP Tasks

1 code implementation EMNLP 2020 Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, Mohit Iyyer

We also develop task embeddings that can be used to predict the most transferable source tasks for a given target task, and we validate their effectiveness in experiments controlled for source and target data size.

Language Modelling Part-Of-Speech Tagging +3

Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning

no code implementations EMNLP 2020 Tao Shen, Yi Mao, Pengcheng He, Guodong Long, Adam Trischler, Weizhu Chen

In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text.

Entity Linking Knowledge Base Completion +4

Role-Wise Data Augmentation for Knowledge Distillation

1 code implementation ICLR 2020 Jie Fu, Xue Geng, Zhijian Duan, Bohan Zhuang, Xingdi Yuan, Adam Trischler, Jie Lin, Chris Pal, Hao Dong

To our knowledge, existing methods overlook the fact that although the student absorbs extra knowledge from the teacher, both models share the same input data -- and this data is the only medium by which the teacher's knowledge can be demonstrated.

Data Augmentation Knowledge Distillation

Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence

1 code implementation9 Sep 2019 Rui Meng, Xingdi Yuan, Tong Wang, Peter Brusilovsky, Adam Trischler, Daqing He

Recently, concatenating multiple keyphrases as a target sequence has been proposed as a new learning paradigm for keyphrase generation.

Keyphrase Generation

Interactive Machine Comprehension with Information Seeking Agents

1 code implementation ACL 2020 Xingdi Yuan, Jie Fu, Marc-Alexandre Cote, Yi Tay, Christopher Pal, Adam Trischler

Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA).

Decision Making Information Retrieval +2

Metalearned Neural Memory

1 code implementation NeurIPS 2019 Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler

We augment recurrent neural networks with an external memory mechanism that builds upon recent progress in metalearning.

Question Answering reinforcement-learning

A Study of State Aliasing in Structured Prediction with RNNs

no code implementations ICLR Workshop drlStructPred 2019 Layla El Asri, Adam Trischler

We show through extensive experiments and analysis that, when trained with policy gradient, recurrent neural networks often fail to learn a state representation that leads to an optimal policy in settings where the same action should be taken at different states.

reinforcement-learning Structured Prediction

An Empirical Study of Example Forgetting during Deep Neural Network Learning

2 code implementations ICLR 2019 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 classification tasks.

General Classification

Towards Text Generation with Adversarially Learned Neural Outlines

no code implementations NeurIPS 2018 Sandeep Subramanian, Sai Rajeswar Mudumba, Alessandro Sordoni, Adam Trischler, Aaron C. Courville, Chris Pal

We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders.

Text Generation

How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG

1 code implementation IJCNLP 2019 Paul Trichelair, Ali Emami, Adam Trischler, Kaheer Suleman, Jackie Chi Kit Cheung

Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG.

Common Sense Reasoning

The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution

1 code implementation ACL 2019 Ali Emami, Paul Trichelair, Adam Trischler, Kaheer Suleman, Hannes Schulz, Jackie Chi Kit Cheung

To explain this performance gap, we show empirically that state-of-the art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision.

Common Sense Reasoning Coreference Resolution +1

Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension

no code implementations ICLR 2019 Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan, Adam Trischler, Andrew McCallum

We harness and extend a recently proposed machine reading comprehension (MRC) model to query for entity states, since these states are generally communicated in spans of text and MRC models perform well in extracting entity-centric spans.

Knowledge Graphs Machine Reading Comprehension +2

A Knowledge Hunting Framework for Common Sense Reasoning

no code implementations EMNLP 2018 Ali Emami, Noelia De La Cruz, Adam Trischler, Kaheer Suleman, Jackie Chi Kit Cheung

We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge.

Common Sense Reasoning

Metalearning with Hebbian Fast Weights

no code implementations12 Jul 2018 Tsendsuren Munkhdalai, Adam Trischler

We unify recent neural approaches to one-shot learning with older ideas of associative memory in a model for metalearning.

One-Shot Learning

Counting to Explore and Generalize in Text-based Games

2 code implementations29 Jun 2018 Xingdi Yuan, Marc-Alexandre Côté, Alessandro Sordoni, Romain Laroche, Remi Tachet des Combes, Matthew Hausknecht, Adam Trischler

We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments.

text-based games

A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge

no code implementations NAACL 2018 Ali Emami, Adam Trischler, Kaheer Suleman, Jackie Chi Kit Cheung

We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA).

Common Sense Reasoning Coreference Resolution +1

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

4 code implementations ICLR 2018 Sandeep Subramanian, Adam Trischler, Yoshua Bengio, Christopher J. Pal

In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model.

Multi-Task Learning Natural Language Inference +2

Boundary Seeking GANs

no code implementations ICLR 2018 R. Devon Hjelm, Athul Paul Jacob, Adam Trischler, Gerry Che, Kyunghyun Cho, Yoshua Bengio

We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.

Scene Understanding Text Generation

Rapid Adaptation with Conditionally Shifted Neurons

no code implementations ICML 2018 Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri, Adam Trischler

We describe a mechanism by which artificial neural networks can learn rapid adaptation - the ability to adapt on the fly, with little data, to new tasks - that we call conditionally shifted neurons.

Few-Shot Image Classification

Variational Bi-LSTMs

no code implementations ICLR 2018 Samira Shabanian, Devansh Arpit, Adam Trischler, Yoshua Bengio

Bidirectional LSTMs (Bi-LSTMs) on the other hand model sequences along both forward and backward directions and are generally known to perform better at such tasks because they capture a richer representation of the data.

Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder

1 code implementation13 Jun 2017 Caglar Gulcehre, Francis Dutil, Adam Trischler, Yoshua Bengio

We investigate the integration of a planning mechanism into an encoder-decoder architecture with an explicit alignment for character-level machine translation.

Machine Translation Translation

A Joint Model for Question Answering and Question Generation

no code implementations5 Jun 2017 Tong Wang, Xingdi Yuan, Adam Trischler

We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents.

Question Answering Question Generation +1

Boundary-Seeking Generative Adversarial Networks

5 code implementations27 Feb 2017 R. Devon Hjelm, Athul Paul Jacob, Tong Che, Adam Trischler, Kyunghyun Cho, Yoshua Bengio

We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.

Scene Understanding Text Generation

Towards Information-Seeking Agents

no code implementations8 Dec 2016 Philip Bachman, Alessandro Sordoni, Adam Trischler

We develop a general problem setting for training and testing the ability of agents to gather information efficiently.

reinforcement-learning

Iterative Alternating Neural Attention for Machine Reading

1 code implementation7 Jun 2016 Alessandro Sordoni, Philip Bachman, Adam Trischler, Yoshua Bengio

We propose a novel neural attention architecture to tackle machine comprehension tasks, such as answering Cloze-style queries with respect to a document.

Ranked #4 on Question Answering on Children's Book Test (Accuracy-NE metric)

Question Answering Reading Comprehension

A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data

1 code implementation ACL 2016 Adam Trischler, Zheng Ye, Xingdi Yuan, Jing He, Phillip Bachman, Kaheer Suleman

The parallel hierarchy enables our model to compare the passage, question, and answer from a variety of trainable perspectives, as opposed to using a manually designed, rigid feature set.

Natural Language Processing Question Answering +1

Synthesis of recurrent neural networks for dynamical system simulation

no code implementations17 Dec 2015 Adam Trischler, Gabriele MT D'Eleuterio

We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task.

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