91 citations · 357 across the 71 of their papers we have counts for
6 papers · 2 filters
QuAC : Question Answering in Context
Eunsol Choi, He He, Mohit Iyyer +5
We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1)…
Revisiting the Importance of Encoding Logic Rules in Sentiment Classification
Kalpesh Krishna, Preethi Jyothi, Mohit Iyyer
We analyze the performance of different sentiment classification models on syntactically complex inputs like A-but-B sentences. The first contribution of this analysis addresses re…
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Mohit Iyyer, John Wieting, Kevin Gimpel +1
We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples. Given a sentence and a target syntactic form (e.g., a constituency pa…
Inducing and Embedding Senses with Scaled Gumbel Softmax
Fenfei Guo, Mohit Iyyer, Jordan Boyd-Graber
Methods for learning word sense embeddings represent a single word with multiple sense-specific vectors. These methods should not only produce interpretable sense embeddings, but s…
Pathologies of Neural Models Make Interpretations Difficult
Shi Feng, Eric Wallace, Alvin Grissom +3
One way to interpret neural model predictions is to highlight the most important input features---for example, a heatmap visualization over the words in an input sentence. In exist…
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer +4
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses var…