Papers by JinJun Xiong

7 papers
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space (D19-1)

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Challenge: Existing approaches to reviewer-paper matching are less effective to deal with the vocabulary mismatch and partial topic overlap between the submission and reviewer.
Approach: They propose to combine the common topic model and abstract topic vectors to model the topics common to the submission and the reviewer's profile while relying on abstract topic vectors.
Outcome: The proposed model improves on the existing model on two datasets.
A Multi-Perspective Architecture for Semantic Code Search (2020.acl-main)

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Challenge: Existing models do not model interactions between code and description until the final step when their global similarity is calculated.
Approach: They propose a multi-perspective cross-lingual neural framework for code–text matching that captures both global and local similarities.
Outcome: The proposed model performs better on the CoNaLa dataset than previous approaches that map code and text to a single joint embedding space.
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus (N19-1)

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Challenge: Existing methods for text style transfer have demonstrated considerable success, but a parallel corpus may not always be available for a transfer task.
Approach: They propose a text style transfer model that uses an attention-based encoder-decoder to transfer a sentence from the source style to the target style.
Outcome: The proposed model outperforms state-of-the-art methods on two different style transfer tasks.
Faceted Hierarchy: A New Graph Type to Organize Scientific Concepts and a Construction Method (D19-53)

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Challenge: faceted concept hierarchy is a structure of parent-child relationships . concepts are expected to be organized in a hierarchical structure for student learning .
Approach: They propose a faceted concept hierarchy that aims to build facets from scientific literature.
Outcome: The proposed hierarchy is more complete than "type-of" relations, and resolves conflicts by maintaining the acyclic structure of a hierarchy.
Document Similarity for Texts of Varying Lengths via Hidden Topics (P18-1)

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Challenge: Existing approaches to measure document similarity are inadequate for document pairs with non-comparable lengths, such as a long document and its summary.
Approach: They propose a document matching approach to bridge the gap between long documents and their abstract information in a common space of hidden topics.
Outcome: The proposed approach outperforms strong baselines on two matching tasks and incorporates domain knowledge to gain further performance improvement.
Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach (2021.acl-long)

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Challenge: Existing methods to measure fine-grained domain relevance are needed for downstream tasks in natural language processing.
Approach: They propose to measure fine-grained domain relevance, defined as the degree that a term is relevant to a given domain.
Outcome: The proposed method outperforms baselines and surpasses professional human performance.
Exploring Semantic Capacity of Terms (2020.emnlp-main)

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Challenge: Existing models that measure semantic capacity of terms are not all considered equal . a good command of semantic capacity will give us more insight into the granularity of terms .
Approach: They propose a model that evaluates semantic capacity of terms if text corpus can provide enough co-occurrence information of terms.
Outcome: The proposed model can evaluate semantic capacity of terms if the corpus can provide enough co-occurrence information of terms.

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