Search Results for author: Weizhe Lin

Found 17 papers, 9 papers with code

PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers

1 code implementation13 Feb 2024 Weizhe Lin, Jingbiao Mei, Jinghong Chen, Bill Byrne

Large Multimodal Models (LMMs) excel in natural language and visual understanding but are challenged by exacting tasks such as Knowledge-based Visual Question Answering (KB-VQA) which involve the retrieval of relevant information from document collections to use in shaping answers to questions.

 Ranked #1 on Retrieval on InfoSeek (using extra training data)

Question Answering Retrieval +1

Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding

1 code implementation14 Nov 2023 Guangyu Yang, Jinghong Chen, Weizhe Lin, Bill Byrne

Minimum Bayes Risk (MBR) decoding can significantly improve translation performance of Multilingual Large Language Models (MLLMs).

Machine Translation NMT +3

Improving hateful memes detection via learning hatefulness-aware embedding space through retrieval-guided contrastive learning

no code implementations14 Nov 2023 Jingbiao Mei, Jinghong Chen, Weizhe Lin, Bill Byrne, Marcus Tomalin

Finally, we demonstrate a retrieval-based hateful memes detection system, which is capable of making hatefulness classification based on data unseen in training from a database.

Contrastive Learning Meme Classification +1

Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering

1 code implementation NeurIPS 2023 Weizhe Lin, Jinghong Chen, Jingbiao Mei, Alexandru Coca, Bill Byrne

FLMR addresses two major limitations in RA-VQA's retriever: (1) the image representations obtained via image-to-text transforms can be incomplete and inaccurate and (2) relevance scores between queries and documents are computed with one-dimensional embeddings, which can be insensitive to finer-grained relevance.

Passage Retrieval Question Answering +2

Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns

no code implementations23 Sep 2023 Alexandru Coca, Bo-Hsiang Tseng, Jinghong Chen, Weizhe Lin, Weixuan Zhang, Tisha Anders, Bill Byrne

Schema-guided dialogue state trackers can generalise to new domains without further training, yet they are sensitive to the writing style of the schemata.

FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering

no code implementations19 Mar 2023 Weizhe Lin, Zhilin Wang, Bill Byrne

The widely used Fact-based Visual Question Answering (FVQA) dataset contains visually-grounded questions that require information retrieval using common sense knowledge graphs to answer.

Common Sense Reasoning Information Retrieval +4

Schema-Guided Semantic Accuracy: Faithfulness in Task-Oriented Dialogue Response Generation

1 code implementation29 Jan 2023 Jinghong Chen, Weizhe Lin, Bill Byrne

We show that SGSAcc can be applied to evaluate utterances generated from a wide range of dialogue actions in the Schema Guided Dialogue (SGD) dataset with good agreement with human judgment.

Natural Language Inference Response Generation

Retrieval Augmented Visual Question Answering with Outside Knowledge

1 code implementation7 Oct 2022 Weizhe Lin, Bill Byrne

The strong retrieval ability of our model significantly reduces the number of retrieved documents needed in training, yielding significant benefits in answer quality and computation required for training.

Answer Generation Passage Retrieval +3

Transformer-Empowered Content-Aware Collaborative Filtering

no code implementations2 Apr 2022 Weizhe Lin, Linjun Shou, Ming Gong, Pei Jian, Zhilin Wang, Bill Byrne, Daxin Jiang

Knowledge graph (KG) based Collaborative Filtering is an effective approach to personalizing recommendation systems for relatively static domains such as movies and books, by leveraging structured information from KG to enrich both item and user representations.

Collaborative Filtering Contrastive Learning +1

Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking

1 code implementation EMNLP 2021 Weizhe Lin, Bo-Hsiang Tseng, Bill Byrne

Dialogue State Tracking is central to multi-domain task-oriented dialogue systems, responsible for extracting information from user utterances.

Dialogue State Tracking Graph Attention +2

Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning

1 code implementation26 Nov 2020 QingBiao Li, Weizhe Lin, Zhe Liu, Amanda Prorok

Our Message-Aware Graph Attention neTwork (MAGAT) is based on a key-query-like mechanism that determines the relative importance of features in the messages received from various neighboring robots.

Graph Attention

Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences

no code implementations NAACL (NUSE) 2021 Zhilin Wang, Weizhe Lin, Xiaodong Wu

While many different aspects of human experiences have been studied by the NLP community, none has captured its full richness.

No, you're not alone: A better way to find people with similar experiences on Reddit

no code implementations WS 2019 Zhilin Wang, Elena Rastorgueva, Weizhe Lin, Xiaodong Wu

This model is built upon the BERT Next Sentence Prediction model and reduces the time complexity for clustering all posts in a corpus from O(n{\^{}}2) to O(n) with respect to the number of posts.

Clustering Sentence

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