Papers by Vadim Sheinin
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. |
QUEST: A Natural Language Interface to Relational Databases (L18-1)
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| Challenge: | Current systems focus on simple queries but neglect nested queries . nesting is a problem in SQL, but there is no easy way to achieve it . |
| Approach: | They propose a system which can handle nested logic queries without restrictions . they propose QUEST, which can cope with nesting queries without restriction . |
| Outcome: | The proposed system outperforms a baseline system by 11% accuracy. |
SQL-to-Text Generation with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing approaches to generate SQL-to-text using seq2seq models do not capture graph-structured information in SQL query. |
| Approach: | They propose a graph-to-sequence model to encode global structure information into node embeddings. |
| Outcome: | The proposed model outperforms the Seq2Seq and Tree2Sq baselines on the WikiSQL and Stackoverflow datasets. |
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. |
A Large Resource of Patterns for Verbal Paraphrases (L18-1)
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| Challenge: | Xu et al., 2015: paraphrases play an important role in natural language understanding . he says it is difficult to propose a paraphrasing relation for natural language processing systems . |
| Approach: | They propose a resource of such paraphrases that can be used to identify hidden paraphrase pairs . they propose to use the resource to identify paraphrase relationships between two words . |
| Outcome: | The proposed resource contains tens of thousands of such pairs and is available for academic purposes. |
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. |
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
| Approach: | They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form . |
| Outcome: | The proposed model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. |