Search Results for author:
Found 18 papers, 6 papers with code
Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem
We introduce the task of implicit offensive text detection in dialogues, where a statement may have either an offensive or non-offensive interpretation, depending on the listener and context.
Pow-Wow: A Dataset and Study on Collaborative Communication in Pommerman
In this work we perform a controlled study of human language use in a competitive team-based game, and search for useful lessons for structuring communication protocol between autonomous agents.
Machine Translation System Selection from Bandit Feedback
Adapting machine translation systems in the real world is a difficult problem.
Emergent Communication with World Models
This provides a visual grounding of the message, similar to an enhanced observation of the world, which may include objects outside of the listening agent's field-of-view.
Meta-learning Extractors for Music Source Separation
We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models.
Ranked #16 on
Music Source Separation
on MUSDB18
A Structured Variational Autoencoder for Contextual Morphological Inflection
Statistical morphological inflectors are typically trained on fully supervised, type-level data.
Hypothesis Only Baselines in Natural Language Inference
We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI).
Gender Bias in Coreference Resolution
We present an empirical study of gender bias in coreference resolution systems.
Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
Neural architectures are prominent in the construction of language models (LMs).
Break it Down for Me: A Study in Automated Lyric Annotation
Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike.
Represent, Aggregate, and Constrain: A Novel Architecture for Machine Reading from Noisy Sources
In order to extract event information from text, a machine reading model must learn to accurately read and interpret the ways in which that information is expressed.
Programming with a Differentiable Forth Interpreter
Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model.
