Semantic role labeling aims to model the predicate-argument structure of a sentence and is often described as answering "Who did what to whom". BIO notation is typically used for semantic role labeling.
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We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
#2 best model for Sentiment Analysis on SST-5 Fine-grained classification
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
DOMAIN ADAPTATION MACHINE TRANSLATION NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING RELATION EXTRACTION SEMANTIC PARSING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING
We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations.
#2 best model for Predicate Detection on CoNLL 2005
However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
CHINESE WORD SEGMENTATION DEPENDENCY PARSING DOCUMENT CLASSIFICATION IMAGE CLASSIFICATION LANGUAGE MODELLING MACHINE TRANSLATION MULTI-TASK LEARNING PART-OF-SPEECH TAGGING SEMANTIC ROLE LABELING SEMANTIC TEXTUAL SIMILARITY SENTENCE CLASSIFICATION SENTIMENT ANALYSIS
Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates.
Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks.