Papers by Ari Rappoport
Simple and Effective Text Simplification Using Semantic and Neural Methods (P18-1)
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| Challenge: | Sentence splitting is a major simplification operation. |
| Approach: | They propose a simple and efficient splitting algorithm based on an automatic semantic parser. |
| Outcome: | The proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification. |
Multitask Parsing Across Semantic Representations (P18-1)
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| Challenge: | UCCA parsing is a test case for multitask learning, with auxiliary tasks AMR, SDP and Universal Dependencies (UD) . Semantic parsers have arguably yet to reach their full potential due to the limited amount of semantically annotated training data. |
| Approach: | They propose a general transition-based parser that can parse UCCA, AMR, SDP and Universal Dependencies (UD) they use a transition-driven learning architecture and a uniform transition-basic learning architecture to train the parsers. |
| Outcome: | The proposed parser improves UCCA, AMR, SDP and Universal Dependencies (UD) parsing over training in English, German and French. |
Semantic Structural Evaluation for Text Simplification (N18-1)
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| Challenge: | Current measures for evaluating text simplification systems focus on lexical aspects, neglecting its structural aspects. |
| Approach: | They propose to use a reference-less automatic evaluation procedure to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output. |
| Outcome: | The proposed measure has a significant correlation with human judgments and is highly comparable with existing measures. |
BLEU is Not Suitable for the Evaluation of Text Simplification (D18-1)
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| Challenge: | BLEU is widely considered to be an informative metric for text-to-text generation . Xu et al. (2016) found that BLUE is not suitable for evaluation of sentence splitting . |
| Approach: | They propose to use BLEU to evaluate sentence splitting as a metric for machine translation . they propose to compare BLUE with a corpus containing multiple structural paraphrases . |
| Outcome: | The proposed BLEU is not suitable for evaluation of sentence splitting . a correlation analysis with human judgments shows low correlation with BLUE . |
Semantic Structural Decomposition for Neural Machine Translation (2020.starsem-1)
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| Challenge: | Existing methods for translation of long sentences are limited by the translation of single sentences to single sentences. |
| Approach: | They propose to use semantic splitting of the source sentence as preprocessing for machine translation. |
| Outcome: | The proposed approach tackles two main limitations of state-of-the-art machine translation. |
Content Differences in Syntactic and Semantic Representation (N19-1)
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| Challenge: | Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. |
| Approach: | They propose to use Universal Dependencies and UCCA as test cases to compare syntactic and semantic schemes. |
| Outcome: | The proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. |