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Found 7 papers, 3 papers with code
LIMIT-BERT : Linguistics Informed Multi-Task BERT
Besides, LIMIT-BERT takes a semi-supervised learning strategy to offer the same large amount of linguistics task data as that for the language model training.
Semantics-Aware Inferential Network for Natural Language Understanding
Taking explicit contextualized semantics as a complementary input, the inferential module of SAIN enables a series of reasoning steps over semantic clues through an attention mechanism.
LIMIT-BERT : Linguistic Informed Multi-Task BERT
In this paper, we present a Linguistic Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistic tasks by Multi-Task Learning (MTL).
Semantics-aware BERT for Language Understanding
The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks.
Ranked #6 on
Natural Language Inference
on SNLI
DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension
Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question.
Concurrent Parsing of Constituency and Dependency
Constituent and dependency representation for syntactic structure share a lot of linguistic and computational characteristics, this paper thus makes the first attempt by introducing a new model that is capable of parsing constituent and dependency at the same time, so that lets either of the parsers enhance each other.
Dual Co-Matching Network for Multi-choice Reading Comprehension
Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure.
Ranked #3 on
Question Answering
on RACE
