Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
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Greatest papers with code
Language Models are Few-Shot Learners
By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
Ranked #1 on
Question Answering
on PIQA
COMMON SENSE REASONING COREFERENCE RESOLUTION DOMAIN ADAPTATION FEW-SHOT LEARNING LANGUAGE MODELLING MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SENTENCE COMPLETION UNSUPERVISED MACHINE TRANSLATION WORD SENSE DISAMBIGUATION
Top-down Tree Long Short-Term Memory Networks
NAACL 2016
• XingxingZhang/td-treelstm
• 
Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have been successfully applied to a variety of sequence modeling tasks.
Recurrent Memory Networks for Language Modeling
In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data.
Dependency Recurrent Neural Language Models for Sentence Completion
Recent work on language modelling has shifted focus from count-based models to neural models.
CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense
• Websail-NU/AQuA
• 
To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.
Ranked #1 on
Common Sense Reasoning
on CODAH
(using extra training data)
COMMON SENSE REASONING QUESTION ANSWERING SENTENCE COMPLETION
Learning Semantically and Additively Compositional Distributional Representations
This paper connects a vector-based composition model to a formal semantics, the Dependency-based Compositional Semantics (DCS).
GePpeTto Carves Italian into a Language Model
We provide a thorough analysis of GePpeTto's quality by means of both an automatic and a human-based evaluation.
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
• jastfkjg/semantic-matching
• 
Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.
INFORMATION RETRIEVAL QUESTION ANSWERING SENTENCE COMPLETION