Research Area
Year Published

430 Results

August 1, 2019

Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives

Annual Meeting of the Association for Computational Linguistics (ACL)

This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty.

By: Yi Tay, Shuohang Wang, Luu Anh Tuan, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang

August 1, 2019

Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification

Workshop on Abusive Language Online

We present a number of fusion approaches to integrate text and photo signals. We show that augmenting text with image embedding information immediately leads to a boost in performance, while applying additional attention fusion methods brings further improvement.

By: Fan Yang, Xiaochang Peng, Gargi Ghosh, Reshef Shilon, Hao Ma, Eider Moore, Goran Predovic

July 29, 2019

Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations

Association for Computational Linguistics (ACL)

Zero-shot translation, translating between language pairs on which a Neural Machine Translation (NMT) system has never been trained, is an emergent property when training the system in multilingual settings. However, naïve training for zero-shot NMT easily fails, and is sensitive to hyper-parameter setting. The performance typically lags far behind the more conventional pivot-based approach which translates twice using a third language as a pivot.

By: Jiatao Gu, Yong Wang, Kyunghyun Cho, Victor O.K. Li

July 29, 2019

Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

To address the challenge of rapidly generating low-level controllers, we argue for using model-based reinforcement learning (MBRL) trained on relatively small amounts of automatically generated (i.e., without system simulation) data. In this paper, we explore the capabilities of MBRL on a Crazyflie centimeter-scale quadrotor with rapid dynamics to predict and control at ≤ 50Hz.

By: Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli, Sergey Levine, Roberto Calandra, Kristofer S. J. Pister

July 29, 2019

Word-order biases in deep-agent emergent communication

Association for Computational Linguistics (ACL)

Sequence-processing neural networks led to remarkable progress on many NLP tasks. As a consequence, there has been increasing interest in understanding to what extent they process language as humans do. We aim here to uncover which biases such models display with respect to “natural” word-order constraints.

By: Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric, Emmanuel Dupoux, Marco Baroni

July 29, 2019

OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs

Association for Computational Linguistics (ACL)

We study a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph (KG) to introduce engaging and contextually diverse entities and attributes. For this study, we collect a new Open-ended Dialog ↔ KG parallel corpus called OpenDialKG, where each utterance from 15K human-to-human role-playing dialogs is manually annotated with ground-truth reference to corresponding entities and paths from a large-scale KG with 1M+ facts.

By: Shane Moon, Pararth Shah, Anuj Kumar, Rajen Subba

July 29, 2019

Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism

Association for Computational Linguistics (ACL)

We introduce the Scratchpad Mechanism, a novel addition to the sequence-to-sequence (seq2seq) neural network architecture and demonstrate its effectiveness in improving the overall fluency of seq2seq models for natural language generation tasks.

By: Ryan Y. Benmalek, Madian Khabsa, Suma Desu, Claire Cardie, Michele Banko

July 29, 2019

Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset

Association for Computational Linguistics (ACL)

One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill. While it is straightforward for humans to recognize and acknowledge others’ feelings in a conversation, this is a significant challenge for AI systems due to the paucity of suitable publicly-available datasets for training and evaluation. This work proposes a new benchmark for empathetic dialogue generation and EMPATHETICDIALOGUES, a novel dataset of 25k conversations grounded in emotional situations.

By: Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau

July 28, 2019

CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks

Annual Meeting of the Association for Computational Linguistics (ACL)

We test here a convolutional network (CNN) on these tasks, reporting hugely improved performance with respect to RNNs. Despite the big improvement, the CNN has however not induced systematic rules, suggesting that the difference between compositional and non-compositional behaviour is not clear-cut.

By: Roberto Dessi, Marco Baroni

July 28, 2019

CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication

Association for Computational Linguistics (ACL)

In this work, we propose a goal-driven collaborative task that combines language, perception, and action. Specifically, we develop a Collaborative image-Drawing game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art objects.

By: Jin-Hwa Kim, Nikita Kitaev, Xinlei Chen, Marcus Rohrbach, Byoung-Tak Zhang, Yuandong Tian, Dhruv Batra, Devi Parikh