Papers by Wei-Yun Ma
Automatic Construction of a Chinese Review Dataset for Aspect Sentiment Triplet Extraction via Iterative Weak Supervision (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is a task that involves the extraction of three key elements: target aspects, descriptive opinion spans, and their corresponding sentiment polarity. |
| Approach: | They propose a framework that facilitates automatic construction of Aspect Sentiment Triplet Extraction (ASTE) by iterative weak supervision and a discriminator to weed out subpar samples. |
| Outcome: | The proposed framework automates the construction of Aspect Sentiment Triplet Extraction tasks in Chinese by using iterative weak supervision. |
iComposer: An Automatic Songwriting System for Chinese Popular Music (N19-4)
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| Challenge: | iComposer is an interactive web-based songwriting system designed to assist human creators by greatly simplifying music production. |
| Approach: | They propose a web-based songwriting system that automatically generates melody from text . they use sequence-to-sequence models to predict melody, rhythm, and lyrics . |
| Outcome: | The proposed system can write pleasing melodies and meaningful lyrics similar to humans. |
CA-EHN: Commonsense Analogy from E-HowNet (2020.lrec-1)
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| Challenge: | Existing word analogy datasets rely on handcrafted words with only dozens of predefined relations. |
| Approach: | They present a commonsense word analogy dataset with 90,505 analogies . they use an ontology that annotates 88K Chinese words with their structured sense definitions and English translations. |
| Outcome: | The proposed dataset shows that word representations embed commonsense knowledge. |
Headword-Oriented Entity Linking: A Special Entity Linking Task with Dataset and Baseline (2020.lrec-1)
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| Challenge: | specialized entity linking problem involves linking only headwords of entities to knowledge bases . full product names are rarely written in context, instead abbreviated to shorter, irregular versions . |
| Approach: | They propose a specialized entity linking problem where only the headwords of entities are to be linked to knowledge bases. |
| Outcome: | The proposed model provides a strong benchmark performance on the special task. |
Generating Attractive and Authentic Copywriting from Customer Reviews (2024.naacl-long)
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| Challenge: | Typical approaches to copywriting focus on product attributes, leading to dull and repetitive content. |
| Approach: | They propose to generate copywriting based on customer reviews as they provide firsthand practical experiences with products, offering a richer source of information than just product attributes. |
| Outcome: | The proposed framework outperforms baseline and zero-shot large language models in terms of both attractiveness and faithfulness. |
Word Embedding Evaluation Datasets and Wikipedia Title Embedding for Chinese (L18-1)
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| Challenge: | Existing evaluation sets for word embeddings in English are limited. |
| Approach: | They propose to translate existing evaluation sets from English to Chinese to evaluate Chinese word embeddings. |
| Outcome: | The proposed evaluation sets are based on translations of popular evaluation sets from English to Chinese and human rating from Amazon Mechanical Turk workers. |
Plug-in Language Model: Controlling Text Generation with a Simple Regression Model (2024.findings-naacl)
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| Challenge: | Large-scale pre-trained language models have demonstrated unrivaled capacity in generating text that closely resembles human-written content. |
| Approach: | They propose a plug-in language model that leverages reinforcement learning to adjust latent states to control text generation. |
| Outcome: | The proposed model outperforms existing methods that rely on gradient-based, weighted decoding, or prompt-based methods. |
Speed Reading: Learning to Read ForBackward via Shuttle (D18-1)
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| Challenge: | LSTM-Shuttle uses human speed reading techniques to perform natural language processing tasks. |
| Approach: | They propose a model which uses human speed reading techniques to perform natural language processing tasks for accurate and efficient comprehension. |
| Outcome: | The proposed model predicts on IMDB, Rotten Tomatoes, AG, and Children’s Book Test datasets and goes backwards. |
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (P19-1)
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| Challenge: | GraphRel is an end-to-end relation extraction model that uses graph convolutional networks to learn named entities and relations. |
| Approach: | They propose a graph-based relation extraction model which uses graph convolutional networks to jointly learn named entities and relations. |
| Outcome: | The proposed model outperforms previous models on two public datasets: NYT and WebNLG. |
H-FND: Hierarchical False-Negative Denoising for Distant Supervision Relation Extraction (2021.findings-acl)
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| Challenge: | Existing work on distant supervision denoising introduces false-positive (FP) and falsenegative (FN) training instances to the generated datasets. |
| Approach: | They propose a hierarchical false-negative denoising framework for distant supervision relation extraction that denoises false-positive and false- negative training instances. |
| Outcome: | The proposed framework can revise FN instances correctly and maintain high F1 scores even when 50% of instances have been turned into negatives. |
Extended HowNet 2.0 – An Entity-Relation Common-Sense Representation Model (L18-1)
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| Challenge: | Extended HowNet 2.0 is a common-sense representation model for lexical senses . |
| Approach: | They propose Extended HowNet 2.0 -an entity-relation common-sense representation model . a query system is being developed for flexibly clustering concepts . |
| Outcome: | The proposed model can bring significant benefits to the community of lexical semantics and natural language understanding. |