Research Area
Year Published

125 Results

August 1, 2019

Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue

Annual Meeting of the Association for Computational Linguistics (ACL)

In this paper, we (1) propose using tree-structured semantic representations, like those used in traditional rule-based NLG systems, for better discourse-level structuring and sentence-level planning; (2) introduce a challenging dataset using this representation for the weather domain; (3) introduce a constrained decoding approach for Seq2Seq models that leverages this representation to improve semantic correctness; and (4) demonstrate promising results on our dataset and the E2E dataset.

By: Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba

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

August 1, 2019

Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks

Annual Meeting of the Association for Computational Linguistics (ACL)

Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks.

By: Yi Tay, Aston Zhang, Luu Anh Tuan, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, Siu Cheung Hui

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

July 29, 2019

Better Character Language Modeling Through Morphology

Association for Computational Linguistics (ACL)

We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition improves bits-per-character (BPC) performance across 24 languages, even when the morphology data and language modeling data are disjoint.

By: Terra Blevins, Luke Zettlemoyer

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

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

How to Get Past Sesame Street: Sentence-Level Pretraining Beyond Language Modeling

Association for Computational Linguistics (ACL)

Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling.

By: Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman