Papers by Gabriel Marzinotto
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora (2020.lrec-1)
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| Challenge: | a recent study has shown that language mismatch and domain mismatch can affect performance of a machine reading task . a factor between language mismatched and domain-mismatched has the strongest influence on performance . |
| Approach: | They compare the cross-language and cross-domain capabilities of BERT on a machine reading comprehension task on two corpora: SQuAD and a new French Machine Reading dataset. |
| Outcome: | The proposed model matches human performance on a machine reading comprehension task with BERT on Chinese and French documents with interesting results. |
Semantic Frame Parsing for Information Extraction : the CALOR corpus (L18-1)
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| Challenge: | a recent study compares the semantic parsing of encyclopedic history texts with the Berkeley FrameNet project. |
| Approach: | They propose to use Berkeley FrameNet to parse encyclopedic history texts . they use a sequence labeling model which optimizes frame identification and role segmentation . |
| Outcome: | The proposed approach leverages the manual annotation of larger corpora than full text parsing. |
Robust Semantic Parsing with Adversarial Learning for Domain Generalization (N19-2)
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| Challenge: | Using adversarial learning to train models on a higher level of abstraction to increase their robustness to lexical and stylistic variations is crucial for the integration of Semantic Parsing technologies in real applications. |
| Approach: | They propose to perform Semantic Parsing with a domain classification adversarial task and an unsupervised domain discovery approach that yields equivalent improvements. |
| Outcome: | The proposed approach improves on a French corpus of encyclopedic documents annotated with FrameNet and an unsupervised domain discovery approach yields equivalent improvements. |