LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention
            Recurrent Neural Networks and Cross Word Alignment for Semantic
            Textual Similarity

Arroyo-Fernández, Ignacio; Meza, Iván

doi:10.18653/v1/s17-2031

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…When it comes to reasoning, specificity of performance metrics may not be characteristic of natural language, especially from the point of view of the open vocabulary inherent in natural language and semantic change (probably due to logical inference and/or synonymy). To account for these points of view, we performed hypothesis testing based on Semantic Textual Similarity (STS) in source and target tasks, which measures reasoning quality in the sense of semantic relatedness [34,29]. We compared to the distribution of STS measurements between predicted and shuffled ground truth object phrases (a random baseline that simulates perturbation of the actual correspondence between subject-predicate and object).…”

Section: Methodsmentioning

confidence: 99%

Common Sense Knowledge Learning for Open Vocabulary Neural Reasoning: A First View into Chronic Disease Literature

Arroyo-Fernández¹,

Armando²,

Téllez-Velázquez³

et al. 2021

Preprint

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In this paper, we address reasoning tasks from open vocabulary Knowledge Bases (openKBs) using state-of-the-art Neural Language Models (NLMs) with applications in scientific literature. For this purpose, self-attention based NLMs are trained using a common sense KB as a source task. The NLMs are then tested on a target KB for open vocabulary reasoning tasks involving scientific knowledge related to the most prevalent chronic diseases (also known as noncommunicable diseases, NCDs). Our results identified NLMs that performed consistently and with significance in knowledge inference for both source and target tasks. Furthermore, in our analysis by inspection we discussed the semantic regularities and reasoning capabilities learned by the models, while showing a first insight into the potential benefits of our approach to aid NCD research.

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Section: Methodsmentioning

confidence: 99%

Common Sense Knowledge Learning for Open Vocabulary Neural Reasoning: A First View into Chronic Disease Literature

Arroyo-Fernández¹,

Armando²,

Téllez-Velázquez³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…The main advantage of such an approach is that there exists the possibility of studying the statistical behavior of sentence meaning. As an additional and important benefit, sentence embeddings make it possible to leverage a number of NLP tasks, such as sentence clustering, text summarization (Zhang et al, 2012;Arroyo-Fernández, 2015;Arroyo-Fernández et al, 2016;Yu et al, 2017), sentence classification (Kalchbrenner et al, 2014;Chen et al, 2017;Er et al, 2016), paraphrase identification (Yin and Schütze, 2015), semantic similarity/ relatedness and sentiment classification (Arroyo-Fernández and Meza Ruiz, 2017;Chen et al, 2017;De Boom et al, 2016;Kalchbrenner et al, 2014;Onan et al, 2017;Yazdani and Popescu-Belis, 2013).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised sentence representations as word information series: Revisiting TF–IDF

Arroyo-Fernández

Méndez-Cruz

Sierra

et al. 2019

Computer Speech & Language

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Sentence representation at the semantic level is a challenging task for Natural Language Processing and Artificial Intelligence. Despite the advances in word embeddings (i.e. word vector representations), capturing sentence meaning is an open question due to complexities of semantic interactions among words. In this paper, we present an embedding method, which is aimed at learning unsupervised sentence representations from unlabeled text. We propose an unsupervised method that models a sentence as a weighted series of word embeddings. The weights of the word embeddings are fitted by using Shannon's word entropies provided by the Term Frequency-Inverse Document Frequency (TF-IDF) transform. The hyperparameters of the model can be selected according to the properties of data (e.g. sentence length and textual gender). Hyperparameter selection involves word embedding methods and dimensionalities, as well as weighting schemata. Our method offers advantages over existing methods: identifiable modules, short-term training, online inference of (unseen) sentence representations, as well as independence from domain, external knowledge and language resources. Results showed that our model outperformed the state of the art in well-known Semantic Textual Similarity (STS) benchmarks. Moreover, our model reached state-of-the-art performance when compared to supervised and knowledge-based STS systems.

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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

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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).

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LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity

Cited by 3 publications

References 6 publications

Common Sense Knowledge Learning for Open Vocabulary Neural Reasoning: A First View into Chronic Disease Literature

Common Sense Knowledge Learning for Open Vocabulary Neural Reasoning: A First View into Chronic Disease Literature

Unsupervised sentence representations as word information series: Revisiting TF–IDF

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

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