Papers by Irene Manotas
Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition (2020.coling-main)
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| Challenge: | Identifying motion entities in text is not only challenging but beneficial for a better natural language understanding. |
| Approach: | They propose a Motion Entity Tagging model to identify entities in motion in a text using the Literal-Motion-in-Text dataset for training and evaluating the model. |
| Outcome: | The proposed method improves the Named-Entity Recognition task by splitting clauses and phrases from complex and long motion sentences. |
Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL (2022.coling-1)
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| Challenge: | Existing attempts on Text-to-SQL task show a dramatic decline in performance for new databases. |
| Approach: | They propose a hybrid system that integrates rule-based and deep learning components to improve model accuracy. |
| Outcome: | The proposed system achieves double-digit percentage improvement for non-Spider databases. |
Tackling Temporal Questions in Natural Language Interface to Databases (2022.emnlp-industry)
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| Challenge: | Temporal aspect is one of the most challenging areas in Natural Language Interface to Databases (NLIDB). |
| Approach: | They propose a dataset with accompanied databases supporting temporal questions in NLIDB. |
| Outcome: | The proposed dataset helps two models learn and improve in temporal aspect. |
LiMiT: The Literal Motion in Text Dataset (2020.findings-emnlp)
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| Challenge: | Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. |
| Approach: | They propose to use a human-annotated dataset to identify motion of physical entities in natural language. |
| Outcome: | The proposed dataset analyzes the scale and diversity of the dataset and provides a baseline model. |