Papers with fully-supervised setting
Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022.naacl-main)
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| Challenge: | Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data. |
| Approach: | They propose a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks. |
| Outcome: | The proposed language model generalizes well across knowledge-grounded dialogue tasks. |
Scaling Neural ITN for Numbers and Temporal Expressions in Tamil: Findings for an Agglutinative Low-resource Language (2023.emnlp-industry)
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| Challenge: | Inverse Text Normalisation (ITN) is a textrewriting task that converts verbalized text to written form. |
| Approach: | They propose to use a seq2seq model, a non-autoregressive text editor and a sequence tagger + rules combination to fine-tune three pre-trained neural models. |
| Outcome: | The proposed model improves with bootstrapping and data augmentation, and bootstrap alone shows a percentage improvement of 14.12 %. |
Enhancing Local Feature Extraction with Global Representation for Neural Text Classification (D19-1)
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| Challenge: | Existing methods for text classification learn long dependency by deeply stacking or hybrid modeling. |
| Approach: | They propose a global-based local feature extraction architecture with global information incorporated into the local feature extractor. |
| Outcome: | The proposed architecture outperforms the previous best models on eight benchmark datasets. |
SymKGQA: Few-Shot Knowledge Graph Question Answering via Symbolic Program Generation and Execution (2024.acl-long)
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| Challenge: | Recent advances in Large Language Models have led to low-level LFs that are limited to the knowledge of underlying LLM about the LF. |
| Approach: | They propose a framework that generates a symbolic LF in a few-shot setting using Large Language Models. |
| Outcome: | The proposed framework outperforms all other few-shot and many fully-supervised KGQA approaches. |
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation (2020.emnlp-main)
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| Challenge: | Existing methods for data-to-text generation rely on labeled data, which is costly to acquire and limits their application to new tasks and domains. |
| Approach: | They propose to leverage pre-training and transfer learning to address this problem by leveraging a general knowledge-grounded generation model and a knowledge-based model. |
| Outcome: | The proposed model can generate knowledge-enriched text on a knowledge-grounded text corpus crawled from the web in three settings. |