Papers with slot filling tasks
SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling (P18-4)
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| Challenge: | SANTO is an annotation tool designed for complex relation extraction tasks . a subset of information extraction tasks can be typed n-ary relation extraction or slot filling . |
| Approach: | They propose a domain-adaptive annotation tool for complex slot filling tasks . SANTO enables fast and clearly structured annotation for multiple users in parallel . |
| Outcome: | The proposed tool can be used for slot filling tasks and import and export procedures of standard formats enable interoperability with external sources and tools. |
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (N18-2)
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| Challenge: | Intent detection and slot filling are two main tasks for building a spoken language understanding system. |
| Approach: | They propose to use a sequence to sequence model to generate both intent and slot filling tasks together to perform the two tasks jointly. |
| Outcome: | The proposed model achieves 0.5% intent accuracy improvement and 0.9 % slot filling improvement on the ATIS benchmark data. |
Few-shot Intent Classification and Slot Filling with Retrieved Examples (2021.naacl-main)
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| Challenge: | Existing methods for few-shot learning are based on labeled examples, but they are non-trivial . few-sshot learning is challenging due to the imbalance in the amount of data between the source and target domains. |
| Approach: | They propose retrieval-based methods for intent classification and slot filling tasks . they use a batch-softmax objective to learn similar contextualized representations for spans . |
| Outcome: | The proposed method outperforms previous systems on the CLINC and SNIPS benchmarks. |
The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain (2020.acl-main)
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Annemarie Friedrich, Heike Adel, Federico Tomazic, Johannes Hingerl, Renou Benteau, Anika Marusczyk, Lukas Lange
| Challenge: | Using BERT embeddings leads to large performance gains, but with increasing task complexity, adding a recurrent neural network seems beneficial. |
| Approach: | They propose an annotation scheme for marking information on publications related to solid oxide fuel cells . they propose to use a recurrent neural network to solve a variety of tasks . |
| Outcome: | The proposed scheme is based on a corpus of 45 open-access scholarly articles and a neural network for a variety of tasks. |
Phrase Retrieval Learns Passage Retrieval, Too (2021.emnlp-main)
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| Challenge: | Dense retrieval methods have shown great promise over sparse methods in a range of NLP problems. |
| Approach: | They propose to use dense phrase retrieval to learn coarse-level retrieval including passages . they show phrase retrievals can be fine-tuned for more coarse-grained retrieval units . |
| Outcome: | The proposed method improves passage retrieval accuracy and QA performance with fewer passages. |
Learning Dense Representations of Phrases at Scale (2021.acl-long)
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| Challenge: | Existing phrase retrieval models rely on sparse representations and still underperform retriever-reader approaches. |
| Approach: | They propose a method to learn phrase representations from reading comprehension tasks using negative sampling methods. |
| Outcome: | The proposed model improves over previous models by 15%-25% absolute accuracy and matches the performance of state-of-the-art retrieval models. |