Papers by Abdalghani Abujabal
Novel Feature Discovery for Task-Oriented Dialog Systems (2023.findings-eacl)
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| Challenge: | Prior work on novelty detection limits the scope of features represented by novel single intents to those represented by multiple user-perceived fine-grained features belonging to the same intent. |
| Approach: | They propose to use a feature discovery technique to discover novel features from user utterances rather than single intent discovery to classify them into slots. |
| Outcome: | The proposed approach consistently detects novel features from user utterances on two datasets. |
Continuous Model Improvement for Language Understanding with Machine Translation (2021.naacl-industry)
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Abdalghani Abujabal, Claudio Delli Bovi, Sungho Ryu, Turan Gojayev, Fabian Triefenbach, Yannick Versley
| Challenge: | a simple translation-test approach would fail the latency requirements of a live environment. |
| Approach: | They show that annotating unlabeled utterances offline can improve performance . they demonstrate that an extrinsic evaluation can improve the performance if manual data is available . |
| Outcome: | The proposed method improves performance in an extrinsic evaluation setting with real-world commercial dialog system in german. |
Identifying and Resolving Annotation Changes for Natural Language Understanding (2021.naacl-industry)
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| Challenge: | Annotation conflict resolution is crucial for machine learning, says a new study . past work on annotation conflict resolution assumed data is collected at once . a a supervised neural model can resolve conflicts in data annotation but requires access to high-quality data . |
| Approach: | They propose an approach to resolve annotation conflicts in a real-world context using a German dialog system. |
| Outcome: | The proposed approach improves on a real-world dataset with 3.5M utterances in German. |
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (N19-1)
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| Challenge: | ComQA dataset captures question phenomena and the diverse ways in which they are formulated. |
| Approach: | They propose a large dataset of real user questions that captures question phenomena and the diverse ways in which they are formulated. |
| Outcome: | The proposed dataset can be a driver of future research on factoid question answering (QA). |