Research Area
Year Published

149 Results

December 14, 2019

From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition

IEEE Automatic Speech Recognition and Understanding Workshop

There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to get competitive performance, especially on English which has poor grapheme-phoneme correspondence. In this work, we show for the first time that, on English, hybrid ASR systems can in fact model graphemes effectively by leveraging tied context-dependent graphemes, i.e., chenones.

By: Duc Le, Xiaohui Zhang, Weiyi Zhang, Christian Fuegen, Geoffrey Zweig, Michael L. Seltzer

November 10, 2019

EASSE: Easier Automatic Sentence Simplification Evaluation

Conference on Empirical Methods in Natural Language Processing (EMNLP)

We introduce EASSE, a Python package aiming to facilitate and standardize automatic evaluation and comparison of Sentence Simplification (SS) systems. EASSE provides a single access point to a broad range of evaluation resources: standard automatic metrics for assessing SS outputs (e.g. SARI), word-level accuracy scores for certain simplification transformations, reference-independent quality estimation features (e.g. compression ratio), and standard test data for SS evaluation (e.g. TurkCorpus).

By: Fernando Alva-Manchego, Louis Martin, Carolina Scarton, Lucia Specia

November 7, 2019

Cloze-driven Pretraining of Self-attention Networks

Conference on Empirical Methods in Natural Language Processing (EMNLP)

We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems.

By: Alexei Baevski, Sergey Edunov, Yinhan Liu, Luke Zettlemoyer, Michael Auli

November 5, 2019

Revisiting the Evaluation of Theory of Mind through Question Answering

Conference on Empirical Methods in Natural Language Processing (EMNLP)

Theory of mind, i.e., the ability to reason about intents and beliefs of agents is an important task in artificial intelligence and central to resolving ambiguous references in natural language dialogue. In this work, we revisit the evaluation of theory of mind through question answering.

By: Matthew Le, Y-Lan Boureau, Maximilian Nickel

November 5, 2019

A Discrete Hard EM Approach for Weakly Supervised Question Answering

Conference on Empirical Methods in Natural Language Processing (EMNLP)

Many question answering (QA) tasks only provide weak supervision for how the answer should be computed. For example, TRIVIAQA answers are entities that can be mentioned multiple times in supporting documents, while DROP answers can be computed by deriving many different equations from numbers in the reference text. In this paper, we show it is possible to convert such tasks into discrete latent variable learning problems with a precomputed, task-specific set of possible solutions (e.g. different mentions or equations) that contains one correct option.

By: Sewon Min, Danqi Chen, Hannaneh Hajishirzi, Luke Zettlemoyer

November 5, 2019

Memory Grounded Conversational Reasoning

Conference on Empirical Methods in Natural Language Processing (EMNLP)

We demonstrate a conversational system which engages the user through a multi-modal, multi-turn dialog over the user’s memories. The system can perform QA over memories by responding to user queries to recall specific attributes and associated media (e.g. photos) of past episodic memories. The system can also make proactive suggestions to surface related events or facts from past memories to make conversations more engaging and natural.

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

November 5, 2019

Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue

Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this work, we collect a goal-driven recommendation dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260 conversation turns between pairs of human workers recommending movies to each other. The task is specifically designed as a cooperative game between two players working towards a quantifiable common goal.

By: Dongyeop Kang, Anusha Balakrishnan, Pararth Shah, Paul A. Crook, Y-Lan Boureau, Jason Weston

November 5, 2019

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

Conference on Empirical Methods in Natural Language Processing (EMNLP)

The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way. In this work, we introduce a diagnostic benchmark suite, named CLUTRR, to clarify some key issues related to the robustness and systematicity of NLU systems.

By: Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, William L. Hamilton

November 4, 2019

Countering Language Drift via Visual Grounding

Conference on Empirical Methods in Natural Language Processing (EMNLP)

Emergent multi-agent communication protocols are very different from natural language and not easily interpretable by humans. We find that agents that were initially pretrained to produce natural language can also experience detrimental language drift: when a nonlinguistic reward is used in a goal-based task, e.g. some scalar success metric, the communication protocol may easily and radically diverge from natural language.

By: Jason Lee, Kyunghyun Cho, Douwe Kiela

November 4, 2019

Emergent Linguistic Phenomena in Multi-Agent Communication Games

Conference on Empirical Methods in Natural Language Processing (EMNLP)

We describe a multi-agent communication framework for examining high-level linguistic phenomena at the community-level. We demonstrate that complex linguistic behavior observed in natural language can be reproduced in this simple setting: i) the outcome of contact between communities is a function of inter- and intra-group connectivity; ii) linguistic contact either converges to the majority protocol, or in balanced cases leads to novel creole languages of lower complexity; and iii) a linguistic continuum emerges where neighboring languages are more mutually intelligible than farther removed languages.

By: Laura Graesser, Kyunghyun Cho, Douwe Kiela