Supervised Learning of Universal Sentence Representations from
            Natural Language Inference Data

Conneau, Alexis; Kiela, Douwe; Schwenk, Holger; Barrault, Loïc; Bordes, Antoine

doi:10.18653/v1/d17-1070

Cited by 1,692 publications

(1,754 citation statements)

References 32 publications

Supporting

Mentioning

1,719

Contrasting

Unclassified

Order By: Relevance

“…Sentence embedding from bi-directional LSTM trained on SNLI (Conneau et al, 2017) 80.1 75.8 C-PHRASE Prediction of syntactic constituent context words (Pham et al, 2015) 74.3 63.9 PV-DBOW Paragraph vectors, Doc2Vec DBOW (Le and Mikolov, 2014;Lau and Baldwin, 2016) 72.2 64.9 Averaged Word Embedding Baselines LexVec Weighted matrix factorization of PPMI (Salle et al, 2016a,b) 68.9 55.8 FastText Skip-gram with sub-word character n-grams (Joulin et al, 2016) 65.3 53.6 Paragram Paraphrase Database (PPDB) fit word embeddings (Wieting et al, 2015) 63.0 50.1 GloVe Word co-occurrence count fit embeddings (Pennington et al, 2014) 52.4 40.6 Word2vec…”

Section: Sts Benchmarkmentioning

confidence: 99%

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Cer¹,

Diab²,

Agirre³

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

1,162

775

View full text Add to dashboard Cite

Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012)(2013)(2014)(2015)(2016)(2017).

show abstract

Section: Sts Benchmarkmentioning

confidence: 99%

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Cer¹,

Diab²,

Agirre³

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

1,162

775

View full text Add to dashboard Cite

show abstract

“…Sentence embeddings Our input sentences x are sentence embeddings obtained by a pretrained sentence encoder (Conneau et al, 2017) (this is different from the sentence encoder in our model). The pretrained sentence encoder is a BiLSTM with max pooling trained on the Stanford Natural Language Inference corpus (Bowman et al, 2015) for textual entailment.…”

Section: Inputsmentioning

confidence: 99%

“…The pretrained sentence encoder is a BiLSTM with max pooling trained on the Stanford Natural Language Inference corpus (Bowman et al, 2015) for textual entailment. Sentence embeddings from this encoder, combined with logistic regression on top, showed good performance in various transfer tasks, such as entailment and caption-image retrieval (Conneau et al, 2017).…”

Section: Inputsmentioning

confidence: 99%

Attentive Interaction Model: Modeling Changes in View in Argumentation

Poddar

Jeon

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

View full text Add to dashboard Cite

We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument, with the goal of predicting if the argument successfully changes the OH's view. The model has two components: (1) vulnerable region detection, an attention model that identifies parts of the OH's reasoning that are amenable to change, and (2) interaction encoding, which identifies the relationship between the content of the OH's reasoning and that of the challenger's argument. Based on evaluation on discussions from the Change My View forum on Reddit, the two components work together to predict an OH's change in view, outperforming several baselines. A posthoc analysis suggests that sentences picked out by the attention model are addressed more frequently by successful arguments than by unsuccessful ones. 1

show abstract

“…These networks are capable of "memorizing" information, thus being able to better represent longer segments of text, without the danger of vanishing/exploding gradients encountered in traditional, normal recurrent neural networks [50]. These types of networks have been successfully used in most NLP tasks [51].…”

Section: Deep Neural Network and Summary Evaluationmentioning

confidence: 99%

Scoring Summaries Using Recurrent Neural Networks

Ruşeţi

Dascălu

Johnson

et al. 2018

Intelligent Tutoring Systems

View full text Add to dashboard Cite

Abstract. Summarization enhances comprehension and is considered an effective strategy to promote and enhance learning and deep understanding of texts. However, summarization is seldom implemented by teachers in classrooms because the manual evaluation requires a lot of effort and time. Although the need for automated support is stringent, there are only a few shallow systems available, most of which rely on basic word/n-gram overlaps. In this paper, we introduce a hybrid model that uses state-of-the-art recurrent neural networks and textual complexity indices to score summaries. Our best model achieves over 55% accuracy for a 3-way classification that measures the degree to which the main ideas from the original text are covered by the summary . Our experiments show that the writing style, represented by the textual complexity indices, together with the semantic content grasped within the summary are the best predictors, when combined. To the best of our knowledge, this is the first work of its kind that uses RNNs for scoring and evaluating summaries.

show abstract

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

Cited by 1,692 publications

References 32 publications

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Attentive Interaction Model: Modeling Changes in View in Argumentation

Scoring Summaries Using Recurrent Neural Networks

Contact Info

Product

Resources

About