MIDAS at SemEval-2020 Task 10: Emphasis Selection Using Label Distribution Learning and Contextual Embeddings
| Autor: | Rakesh Gosangi, Hemant Yadav, Sarthak Anand, Pradyumna Gupta, Debanjan Mahata, Rajiv Ratn Shah, Haimin Zhang |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Matching (statistics) Computer Science - Computation and Language Computer science business.industry computer.software_genre Part of speech Sequence labeling SemEval Task (project management) Selection (linguistics) Embedding Artificial intelligence business Computation and Language (cs.CL) computer Natural language processing Sentence Word (computer architecture) |
| Zdroj: | SemEval@COLING |
| Popis: | This paper presents our submission to the SemEval 2020 - Task 10 on emphasis selection in written text. We approach this emphasis selection problem as a sequence labeling task where we represent the underlying text with various contextual embedding models. We also employ label distribution learning to account for annotator disagreements. We experiment with the choice of model architectures, trainability of layers, and different contextual embeddings. Our best performing architecture is an ensemble of different models, which achieved an overall matching score of 0.783, placing us 15th out of 31 participating teams. Lastly, we analyze the results in terms of parts of speech tags, sentence lengths, and word ordering. |
| Databáze: | OpenAIRE |
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