Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

ICLR 2018 • facebookresearch/InferSent •

In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model.

MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SEMANTIC TEXTUAL SIMILARITY

Universal Sentence Encoder

29 Mar 2018 • facebookresearch/InferSent •

For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.

SEMANTIC TEXTUAL SIMILARITY SENTENCE EMBEDDINGS SENTIMENT ANALYSIS SUBJECTIVITY ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING WORD EMBEDDINGS

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

EMNLP 2017 • facebookresearch/InferSent •

Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features.

CROSS-LINGUAL NATURAL LANGUAGE INFERENCE SEMANTIC TEXTUAL SIMILARITY TRANSFER LEARNING WORD EMBEDDINGS

Improving Language Understanding by Generative Pre-Training

Preprint 2018 • openai/finetune-transformer-lm

We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task.

DOCUMENT CLASSIFICATION LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY

Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering

COLING 2018 • lanwuwei/SPM_toolkit •

In this paper, we analyze several neural network designs (and their variations) for sentence pair modeling and compare their performance extensively across eight datasets, including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks.

NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTENCE PAIR MODELING

Character-based Neural Networks for Sentence Pair Modeling

HLT 2018 • lanwuwei/SPM_toolkit •

Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference.

NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SEMANTIC TEXTUAL SIMILARITY SENTENCE PAIR MODELING

Determining Semantic Textual Similarity using Natural Deduction Proofs

EMNLP 2017 • mynlp/ccg2lambda

Determining semantic textual similarity is a core research subject in natural language processing.

SEMANTIC TEXTUAL SIMILARITY

Counter-fitting Word Vectors to Linguistic Constraints

HLT 2016 • nmrksic/counter-fitting

In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity.

DIALOGUE STATE TRACKING SEMANTIC TEXTUAL SIMILARITY

MMFeat: A Toolkit for Extracting Multi-Modal Features

ACL 2016 • douwekiela/mmfeat

SEMANTIC TEXTUAL SIMILARITY

Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks

WS 2017 • nathanshartmann/portuguese_word_embeddings

Word embeddings have been found to provide meaningful representations for words in an efficient way; therefore, they have become common in Natural Language Processing sys- tems.

SEMANTIC TEXTUAL SIMILARITY WORD EMBEDDINGS