Search Results for author: Weiwei Cheng

Found 10 papers, 1 papers with code

semiPQA: A Study on Product Question Answering over Semi-structured Data

no code implementations ECNLP (ACL) 2022 Xiaoyu Shen, Gianni Barlacchi, Marco del Tredici, Weiwei Cheng, Adrià Gispert

To fill in this blank, here we study how to effectively incorporate semi-structured answer sources for PQA and focus on presenting answers in a natural, fluent sentence.

Question Answering

Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?

1 code implementation17 Sep 2021 Julia Rozanova, Deborah Ferreira, Krishna Dubba, Weiwei Cheng, Dell Zhang, Andre Freitas

Even though BERT and similar pre-trained language models have excelled in several NLP tasks, their use has not been widely explored for the UI grounding domain.

Multiplicative Tree-Structured Long Short-Term Memory Networks for Semantic Representations

no code implementations SEMEVAL 2018 Nam Khanh Tran, Weiwei Cheng

In addition to syntactic trees, we also investigate the use of Abstract Meaning Representation in tree-structured models, in order to incorporate both syntactic and semantic information from the sentence.

Learning Semantic Representations Machine Translation +3

Salience Rank: Efficient Keyphrase Extraction with Topic Modeling

no code implementations ACL 2017 Nedelina Teneva, Weiwei Cheng

Topical PageRank (TPR) uses latent topic distribution inferred by Latent Dirichlet Allocation (LDA) to perform ranking of noun phrases extracted from documents.

Keyphrase Extraction Part-Of-Speech Tagging +1

On the Bayes-optimality of F-measure maximizers

no code implementations17 Oct 2013 Willem Waegeman, Krzysztof Dembczynski, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hullermeier

The F-measure, which has originally been introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-label classification, and structured output prediction.

General Classification Information Retrieval +2

An Exact Algorithm for F-Measure Maximization

no code implementations NeurIPS 2011 Krzysztof J. Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier

The F-measure, originally introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-label classification, and structured output prediction.

Classification General Classification +3

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