Gating Mechanisms for Combining Character and Word-level Word Representations: an Empirical Study
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[1] Evgeniy Gabrilovich,et al. A word at a time: computing word relatedness using temporal semantic analysis , 2011, WWW.
[2] Bo Pang,et al. Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.
[3] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[4] John B. Goodenough,et al. Contextual correlates of synonymy , 1965, CACM.
[5] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[6] Wang Ling,et al. Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation , 2015, EMNLP.
[7] Gerard de Melo,et al. Exploring Semantic Properties of Sentence Embeddings , 2018, ACL.
[8] Yoshua Bengio,et al. Feature-wise transformations , 2018, Distill.
[9] Eneko Agirre,et al. SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation , 2016, *SEMEVAL.
[10] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[11] Samuel R. Bowman,et al. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference , 2017, NAACL.
[12] Yonatan Belinkov,et al. Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks , 2016, ICLR.
[13] Dan Roth,et al. Learning Question Classifiers , 2002, COLING.
[14] Guillaume Lample,et al. What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties , 2018, ACL.
[15] Sanjeev Arora,et al. A Simple but Tough-to-Beat Baseline for Sentence Embeddings , 2017, ICLR.
[16] Guillaume Lample,et al. Neural Architectures for Named Entity Recognition , 2016, NAACL.
[17] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[18] Wojciech Czarnecki,et al. How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks , 2017, ArXiv.
[19] Christopher D. Manning,et al. Better Word Representations with Recursive Neural Networks for Morphology , 2013, CoNLL.
[20] Luke S. Zettlemoyer,et al. Deep Contextualized Word Representations , 2018, NAACL.
[21] Ehud Rivlin,et al. Placing search in context: the concept revisited , 2002, TOIS.
[22] Peter Young,et al. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions , 2014, TACL.
[23] Felix Hill,et al. SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity , 2016, EMNLP.
[24] Christopher Potts,et al. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.
[25] Phil Blunsom,et al. Compositional Morphology for Word Representations and Language Modelling , 2014, ICML.
[26] Sanja Fidler,et al. Skip-Thought Vectors , 2015, NIPS.
[27] Eneko Agirre,et al. SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation , 2017, *SEMEVAL.
[28] Welch Bl. THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .
[29] Neville Ryant,et al. A large-scale classification of English verbs , 2008, Lang. Resour. Evaluation.
[30] Jürgen Schmidhuber,et al. Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.
[31] Anna Gladkova,et al. Intrinsic Evaluations of Word Embeddings: What Can We Do Better? , 2016, RepEval@ACL.
[32] Tomas Mikolov,et al. Enriching Word Vectors with Subword Information , 2016, TACL.
[33] Kyunghyun Cho,et al. Gated Word-Character Recurrent Language Model , 2016, EMNLP.
[34] Wes McKinney,et al. Data Structures for Statistical Computing in Python , 2010, SciPy.
[35] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[36] Michael P. Rogers. Python Tutorial , 2009 .
[37] Han Zhao,et al. Self-Adaptive Hierarchical Sentence Model , 2015, IJCAI.
[38] Elia Bruni,et al. Multimodal Distributional Semantics , 2014, J. Artif. Intell. Res..
[39] Felix Hill,et al. SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation , 2014, CL.
[40] Yulia Tsvetkov,et al. Problems With Evaluation of Word Embeddings Using Word Similarity Tasks , 2016, RepEval@ACL.
[41] Marco Marelli,et al. A SICK cure for the evaluation of compositional distributional semantic models , 2014, LREC.
[42] Evgeniy Gabrilovich,et al. Large-scale learning of word relatedness with constraints , 2012, KDD.
[43] Christopher D. Manning,et al. Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models , 2016, ACL.
[44] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[45] Yoav Goldberg,et al. The Interplay of Semantics and Morphology in Word Embeddings , 2017, EACL.
[46] Cyrus Rashtchian,et al. Collecting Image Annotations Using Amazon’s Mechanical Turk , 2010, Mturk@HLT-NAACL.
[47] Bo Pang,et al. A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.
[48] Wei Li,et al. Learning Universal Sentence Representations with Mean-Max Attention Autoencoder , 2018, EMNLP.
[49] Alec Radford,et al. Improving Language Understanding by Generative Pre-Training , 2018 .
[50] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[51] Christopher D. Manning,et al. Baselines and Bigrams: Simple, Good Sentiment and Topic Classification , 2012, ACL.
[52] Ye Yuan,et al. Words or Characters? Fine-grained Gating for Reading Comprehension , 2016, ICLR.
[53] Emmanuel Dupoux,et al. Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies , 2016, TACL.
[54] Guillaume Lample,et al. Evaluation of Word Vector Representations by Subspace Alignment , 2015, EMNLP.
[55] Zhe Gan,et al. Learning Generic Sentence Representations Using Convolutional Neural Networks , 2016, EMNLP.
[56] Christopher Potts,et al. A large annotated corpus for learning natural language inference , 2015, EMNLP.
[57] Christiane Fellbaum,et al. Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.
[58] John D. Hunter,et al. Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.
[59] Holger Schwenk,et al. Supervised Learning of Universal Sentence Representations from Natural Language Inference Data , 2017, EMNLP.
[60] Christian S. Perone,et al. Evaluation of sentence embeddings in downstream and linguistic probing tasks , 2018, ArXiv.
[61] Bing Liu,et al. Mining and summarizing customer reviews , 2004, KDD.
[62] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[63] Sampo Pyysalo,et al. Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance , 2016, RepEval@ACL.
[64] Claire Cardie,et al. Annotating Expressions of Opinions and Emotions in Language , 2005, Lang. Resour. Evaluation.
[65] Eric Jones,et al. SciPy: Open Source Scientific Tools for Python , 2001 .
[66] Christopher Joseph Pal,et al. Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning , 2018, ICLR.
[67] Felix Hill,et al. Learning Distributed Representations of Sentences from Unlabelled Data , 2016, NAACL.
[68] Douwe Kiela,et al. No Training Required: Exploring Random Encoders for Sentence Classification , 2019, ICLR.
[69] Aaron C. Courville,et al. FiLM: Visual Reasoning with a General Conditioning Layer , 2017, AAAI.
[70] Omer Levy,et al. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.
[71] Eneko Agirre,et al. A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches , 2009, NAACL.
[72] Alexander M. Rush,et al. Character-Aware Neural Language Models , 2015, AAAI.
[73] Thomas A. Schreiber,et al. The University of South Florida free association, rhyme, and word fragment norms , 2004, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.
[74] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[75] Douwe Kiela,et al. SentEval: An Evaluation Toolkit for Universal Sentence Representations , 2018, LREC.
[76] Samuel R. Bowman,et al. Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning , 2017, ArXiv.