EQuANt extends QANet to SQuAD 2, achieving nearly twice the performance of a lightweight QANet baseline while also improving SQuAD 1.1 results via multi-task learning.
Gated-Attention Readers for Text Comprehension
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
In this paper we study the problem of answering cloze-style questions over documents. Our model, the Gated-Attention (GA) Reader, integrates a multi-hop architecture with a novel attention mechanism, which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader. This enables the reader to build query-specific representations of tokens in the document for accurate answer selection. The GA Reader obtains state-of-the-art results on three benchmarks for this task--the CNN \& Daily Mail news stories and the Who Did What dataset. The effectiveness of multiplicative interaction is demonstrated by an ablation study, and by comparing to alternative compositional operators for implementing the gated-attention. The code is available at https://github.com/bdhingra/ga-reader.
fields
cs.CL 3verdicts
UNVERDICTED 3representative citing papers
LLMs struggle with abstract meaning comprehension on SemEval-2021 Task 4 more than fine-tuned models, and a new bidirectional attention classifier yields small accuracy gains of 3-4%.
A 2019 survey of machine reading comprehension corpora and methods.
citing papers explorer
-
EQuANt (Enhanced Question Answer Network)
EQuANt extends QANet to SQuAD 2, achieving nearly twice the performance of a lightweight QANet baseline while also improving SQuAD 1.1 results via multi-task learning.
-
LLMs Struggle with Abstract Meaning Comprehension More Than Expected
LLMs struggle with abstract meaning comprehension on SemEval-2021 Task 4 more than fine-tuned models, and a new bidirectional attention classifier yields small accuracy gains of 3-4%.
-
Machine Reading Comprehension: a Literature Review
A 2019 survey of machine reading comprehension corpora and methods.