Search Results for author: Yik-Cheung Tam

Found 10 papers, 5 papers with code

Exploring an LM to generate Prolog Predicates from Mathematics Questions

no code implementations7 Sep 2023 Xiaocheng Yang, Yik-Cheung Tam

Consequently, we employ chain-of-thought to fine-tune LLaMA7B as a baseline model and develop other fine-tuned LLaMA7B models for the generation of Prolog code, Prolog code + chain-of-thought, and chain-of-thought + Prolog code, respectively.

GSM8K Language Modelling

Suffix Retrieval-Augmented Language Modeling

1 code implementation6 Nov 2022 Zecheng Wang, Yik-Cheung Tam

SUREALM employs an embedding retriever to search for training sentences in a data store that share similar word history during sequence generation.

Causal Language Modeling Language Modelling +2

UNITER-Based Situated Coreference Resolution with Rich Multimodal Input

1 code implementation7 Dec 2021 Yichen Huang, Yuchen Wang, Yik-Cheung Tam

Our model ranks second in the official evaluation on the object coreference resolution task with an F1 score of 73. 3% after model ensembling.

coreference-resolution Object +1

Ontology-Enhanced Slot Filling

no code implementations25 Aug 2021 Yuhao Ding, Yik-Cheung Tam

In multi-domain task-oriented dialog system, user utterances and system responses may mention multiple named entities and attributes values.

dialog state tracking slot-filling +1

Keyword-Attentive Deep Semantic Matching

1 code implementation11 Mar 2020 Changyu Miao, Zhen Cao, Yik-Cheung Tam

Deep Semantic Matching is a crucial component in various natural language processing applications such as question and answering (QA), where an input query is compared to each candidate question in a QA corpus in terms of relevance.

Retrieval Text Matching

Read and Comprehend by Gated-Attention Reader with More Belief

no code implementations NAACL 2018 Haohui Deng, Yik-Cheung Tam

GA Reader makes two assumptions: (1) a uni-directional attention that uses an input query to gate token encodings of a document; (2) encoding at the cloze position of an input query is considered for answer prediction.

Position Reading Comprehension +1

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