Research Area
Year Published

1012 Results

November 9, 2019

Harassment in Social Virtual Reality: Challenges for Platform Governance

Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)

In immersive virtual reality (VR) environments, experiences of harassment can be exacerbated by features such as synchronous voice chat, heightened feelings of presence and embodiment, and avatar movements that can feel like violations of personal space (such as simulated touching or grabbing). Simultaneously, efforts to govern these developing spaces are made more complex by the distributed landscape of virtual reality applications and the dynamic nature of local community norms. To better understand this nascent social and psychological environment, we interviewed VR users (n=25) about their experiences with harassment, abuse, and discomfort in social VR.

By: Lindsay Blackwell, Nicole Ellison, Natasha Elliott-Deflo, Raz Schwartz

November 7, 2019

Feature Selection for Facebook Feed Ranking System via a Group-Sparsity-Regularized Training Algorithm

Conference on Information and Knowledge Management (CIKM)

In modern production platforms, large scale online learning models are applied to data of very high dimension. To save computational resource, it is important to have an efficient algorithm to select the most significant features from an enormous feature pool. In this paper, we propose a novel neural-network-suitable feature selection algorithm, which selects important features from the input layer during training.

By: Xiuyan Ni, Yang Yu, Peng Wu, Youlin Li, Shaoliang Nie, Qichao Que, Chao Chen

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

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

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

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

Quantifying the Semantic Core of Gender Systems

Conference on Empirical Methods in Natural Language Processing (EMNLP)

We present the first large-scale investigation of the arbitrariness of noun–gender assignments. To that end, we use canonical correlation analysis to correlate the grammatical gender of inanimate nouns with an externally grounded definition of their lexical semantics. We find that 18 languages exhibit a significant correlation between grammatical gender and lexical semantics.

By: Adina Williams, Ryan Cotterell, Lawrence Wolf-Sonkin, Damián E. Blasi, Hanna Wallach

November 4, 2019

Finding Generalizable Evidence by Learning to Convince Q&A Models

Conference on Empirical Methods in Natural Language Processing (EMNLP)

We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed. We train evidence agents to select the passage sentences that most convince a pretrained QA model of a given answer, if the QA model received those sentences instead of the full passage.

By: Ethan Perez, Siddharth Karamcheti, Rob Fergus, Jason Weston, Douwe Kiela, Kyunghyun Cho