1.7k citations · 3k across the 42 of their papers we have counts for
46 papers
Responsible AI Considerations in Text Summarization Research: A Review of Current Practices
Yu Lu Liu, Meng Cao, Su Lin Blodgett +3
AI and NLP publication venues have increasingly encouraged researchers to reflect on possible ethical considerations, adverse impacts, and other responsible AI issues their work mi…
Joint Prompt Optimization of Stacked LLMs using Variational Inference
Alessandro Sordoni, Xingdi Yuan, Marc-Alexandre Côté +6
Large language models (LLMs) can be seen as atomic units of computation mapping sequences to a distribution over sequences. Thus, they can be seen as stochastic language layers in…
Think Before You Act: Decision Transformers with Working Memory
Jikun Kang, Romain Laroche, Xingdi Yuan +3
Decision Transformer-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance relies on massive data and computation. We…
Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective
Ian Porada, Alexandra Olteanu, Kaheer Suleman +2
It is increasingly common to evaluate the same coreference resolution (CR) model on multiple datasets. Do these multi-dataset evaluations allow us to draw meaningful conclusions ab…
The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources in Natural Language Understanding Systems
Akshatha Arodi, Martin Pömsl, Kaheer Suleman +3
Many state-of-the-art natural language understanding (NLU) models are based on pretrained neural language models. These models often make inferences using information from multiple…
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications
Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler +3
There are many ways to express similar things in text, which makes evaluating natural language generation (NLG) systems difficult. Compounding this difficulty is the need to assess…
Modeling Event Plausibility with Consistent Conceptual Abstraction
Ian Porada, Kaheer Suleman, Adam Trischler +1
Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models -- most recently pre-tra…
An Analysis of Dataset Overlap on Winograd-Style Tasks
Ali Emami, Adam Trischler, Kaheer Suleman +1
The Winograd Schema Challenge (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR). Model performance on the WSC has quickly progress…
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté +3
Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of succe…
An Empirical Study on Neural Keyphrase Generation
Rui Meng, Xingdi Yuan, Tong Wang +3
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. Mode…
Exploring and Predicting Transferability across NLP Tasks
Tu Vu, Tong Wang, Tsendsuren Munkhdalai +5
Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. Can fine-tuning these models on tasks other…
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning
Tao Shen, Yi Mao, Pengcheng He +3
In this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledg…
Role-Wise Data Augmentation for Knowledge Distillation
Jie Fu, Xue Geng, Zhijian Duan +6
Knowledge Distillation (KD) is a common method for transferring the ``knowledge'' learned by one machine learning model (the \textit{teacher}) into another model (the \textit{stude…
Learning Dynamic Belief Graphs to Generalize on Text-Based Games
Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté +7
Playing text-based games requires skills in processing natural language and sequential decision making. Achieving human-level performance on text-based games remains an open challe…
Building Dynamic Knowledge Graphs from Text-based Games
Mikuláš Zelinka, Xingdi Yuan, Marc-Alexandre Côté +2
We are interested in learning how to update Knowledge Graphs (KG) from text. In this preliminary work, we propose a novel Sequence-to-Sequence (Seq2Seq) architecture to generate el…
Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence
Rui Meng, Xingdi Yuan, Tong Wang +3
Recently, concatenating multiple keyphrases as a target sequence has been proposed as a new learning paradigm for keyphrase generation. Existing studies concatenate target keyphras…
Interactive Language Learning by Question Answering
Xingdi Yuan, Marc-Alexandre Cote, Jie Fu +4
Humans observe and interact with the world to acquire knowledge. However, most existing machine reading comprehension (MRC) tasks miss the interactive, information-seeking componen…
Interactive Machine Comprehension with Information Seeking Agents
Xingdi Yuan, Jie Fu, Marc-Alexandre Cote +3
Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue t…
Metalearned Neural Memory
Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang +1
We augment recurrent neural networks with an external memory mechanism that builds upon recent progress in metalearning. We conceptualize this memory as a rapidly adaptable functio…
A Study of State Aliasing in Structured Prediction with RNNs
Layla El Asri, Adam Trischler
End-to-end reinforcement learning agents learn a state representation and a policy at the same time. Recurrent neural networks (RNNs) have been trained successfully as reinforcemen…
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes +3
Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understa…
How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG
Paul Trichelair, Ali Emami, Adam Trischler +2
Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG. The question we ask in…
The Knowref Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution
Ali Emami, Paul Trichelair, Adam Trischler +3
We introduce a new benchmark for coreference resolution and NLI, Knowref, that targets common-sense understanding and world knowledge. Previous coreference resolution tasks can lar…
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension
Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan +2
We propose a neural machine-reading model that constructs dynamic knowledge graphs from procedural text. It builds these graphs recurrently for each step of the described procedure…