343 citations · 894 across the 51 of their papers we have counts for
9 papers · 1 filter
Grounding learning of modifier dynamics: An application to color naming
Xudong Han, Philip Schulz, Trevor Cohn
Grounding is crucial for natural language understanding. An important subtask is to understand modified color expressions, such as 'dirty blue'. We present a model of color modifie…
Deep Ordinal Regression for Pledge Specificity Prediction
Shivashankar Subramanian, Trevor Cohn, Timothy Baldwin
Many pledges are made in the course of an election campaign, forming important corpora for political analysis of campaign strategy and governmental accountability. At present, ther…
Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
Zenan Zhai, Dat Quoc Nguyen, Saber A. Akhondi +5
Chemical patents are an important resource for chemical information. However, few chemical Named Entity Recognition (NER) systems have been evaluated on patent documents, due in pa…
Semi-supervised Stochastic Multi-Domain Learning using Variational Inference
Yitong Li, Timothy Baldwin, Trevor Cohn
Supervised models of NLP rely on large collections of text which closely resemble the intended testing setting. Unfortunately matching text is often not available in sufficient qua…
Target Based Speech Act Classification in Political Campaign Text
Shivashankar Subramanian, Trevor Cohn, Timothy Baldwin
We study pragmatics in political campaign text, through analysis of speech acts and the target of each utterance. We propose a new annotation schema incorporating domain-specific s…
Contextualization of Morphological Inflection
Ekaterina Vylomova, Ryan Cotterell, Timothy Baldwin +2
Critical to natural language generation is the production of correctly inflected text. In this paper, we isolate the task of predicting a fully inflected sentence from its partiall…
A Unified Neural Architecture for Instrumental Audio Tasks
Steven Spratley, Daniel Beck, Trevor Cohn
Within Music Information Retrieval (MIR), prominent tasks -- including pitch-tracking, source-separation, super-resolution, and synthesis -- typically call for specialised methods,…
Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations
Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn
Crowd-sourcing is a cheap and popular means of creating training and evaluation datasets for machine learning, however it poses the problem of `truth inference', as individual work…
Massively Multilingual Transfer for NER
Afshin Rahimi, Yuan Li, Trevor Cohn
In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few…