Papers with DBPedia
CoCoID: Learning Contrastive Representations and Compact Clusters for Semi-Supervised Intent Discovery (2022.emnlp-industry)
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| Challenge: | Existing approaches to intent discovery cluster novel intents with prior knowledge from intent-labeled data in a semi-supervised way. |
| Approach: | They propose a semi-supervised intent discovery framework CoCoID with two components . they propose to discriminate user utterance representation learning and intra-cluster knowledge distillation . |
| Outcome: | The proposed framework outperforms state-of-the-art intent discovery models by over 1.4 ACC and ARI points and 1.1 NMI points across four datasets. |
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)
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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang
| Challenge: | Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph . |
| Approach: | They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm. |
| Outcome: | The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy. |
Bootstrapping Generators from Noisy Data (N18-1)
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| Challenge: | Existing methods for data-to-text generation focus on learning correspondences between structured data and associated texts. |
| Approach: | They aim to bootstrap generators from large scale datasets where data and related texts are loosely aligned. |
| Outcome: | The proposed model improves on a vanilla encoder-decoder which relies on soft attention. |
Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models (2023.eacl-main)
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| Challenge: | Pretrained language models (PLMs) for data-to-text generation produce inaccurate outputs if labels are ambiguous or incomplete, which is often the case in D2T datasets. |
| Approach: | They propose to use a dataset to descib a relation between two entities using relation labels to train pretrained language models. |
| Outcome: | The proposed models are robust to generalizing to out-of-domain domains on a dataset for descibing a relation between two entities. |