Papers by Jimmy Xiangji Huang
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization (2020.coling-main)
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| Challenge: | Existing methods to generate abstractive summarizations are lacking labeled training datasets. |
| Approach: | They propose a weakly supervised approach to generate a strong summary from a set of documents based on a query. |
| Outcome: | The proposed approach sets a new state-of-the-art in terms of evaluation metrics on the Document Understanding Conferences dataset. |
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task (2020.lrec-1)
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| Challenge: | Word embeddings that consider context have attracted great attention for natural language processing tasks in recent years. |
| Approach: | They propose two different approaches to integrate contextualized word embeddings with transformer encoders for sentence similarity modeling. |
| Outcome: | The proposed model outperforms the feature-based approach on six datasets. |
ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding (2020.acl-main)
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| Challenge: | Existing knowledge graph embedding methods are limited in their expressiveness and lack structural information in the embeddable space. |
| Approach: | They propose to use a relation-aware network to learn query embedding . they first explore the Inception network to further increase interactions between head and relation embedders . |
| Outcome: | The proposed network improves performance on WN18RR and FB15k-237 datasets. |
A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment (2020.coling-main)
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| Challenge: | Existing methods for cross-lingual entity alignment ignore useful pre-aligned links between two KGs. |
| Approach: | They propose a novel method that jointly learns embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. |
| Outcome: | The proposed method achieves remarkable performance gains on three benchmark cross-lingual entity alignment datasets. |