Sparsifying Word Representations for Deep Unordered Sentence Modeling
Code (0)
등록된 구현이 없습니다.
Tasks
Document ClassificationRepresentation LearningSentenceSentence EmbeddingsWord EmbeddingsSimilar Papers 제목 키워드 기반
Structuring an unordered text document
Segmenting an unordered text document into different sections is a very useful task in many text processing applications like multiple document summarization, question answering, etc. This paper proposes structuring of a…
Document SummarizationQuestion AnsweringSentenceLabel2Label: A Language Modeling Framework for Multi-Attribute Learning
Objects are usually associated with multiple attributes, and these attributes often exhibit high correlations. Modeling complex relationships between attributes poses a great challenge for multi-attribute learning. This …
AttributeClothing Attribute RecognitionFacial Attribute ClassificationLanguage Modeling+3Character-based Neural Networks for Sentence Pair Modeling
Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference. Most state-of-the-art neural models for these tasks rely on pretraine…
Language ModelingLanguage ModellingNatural Language InferenceParaphrase Identification+4Gating Mechanisms for Combining Character and Word-level Word Representations: An Empirical Study
In this paper we study how different ways of combining character and word-level representations affect the quality of both final word and sentence representations. We provide strong empirical evidence that modeling chara…
Semantic SimilaritySemantic Textual SimilaritySentenceWord SimilarityLow-Rank Approximations of Second-Order Document Representations
Document embeddings, created with methods ranging from simple heuristics to statistical and deep models, are widely applicable. Bag-of-vectors models for documents include the mean and quadratic approaches (Torki, 2018).…
ArticlesSentence