Papers with machine-learning algorithms
Automated Evaluation of Out-of-Context Errors (L18-1)
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| Challenge: | Existing methods to modify text understanding systems use only one sentence at a time . however, considering a larger context can improve performance for text understanding tasks. |
| Approach: | They propose to modify existing text data to insert out-of-context errors . they use a 2016 TEDTalk corpus to evaluate computational models for text understanding . |
| Outcome: | The proposed method targets real-world problems of transcription and translation systems by inserting authentic out-of-context errors. |
A Multilingual Approach to Question Classification (L18-1)
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| Challenge: | Existing work on questions has focused on understanding the structure of questions per se . a few approaches explicitly focus on information-seeking questions, but this work is either based on big data or crowdsourcing. |
| Approach: | They propose a dependency-parsed, parallel multilingual corpus of information-seeking and non-information-seeing questions . they employ a linguistically motivated rule-based system that uses linguistic cues from one language to help classify questions across other languages. |
| Outcome: | The proposed system correctly classifies questions in 79% of cases, compared to other systems. |