Papers by Tiejun Ma

Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers (2023.findings-acl)

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Wanjun Zhong, Tingting Ma, Jiahai Wang, Jian Yin, Tiejun Zhao, Chin-Yew Lin, Nan Duan

Challenge:	ReasonFormer is a unified reasoning framework for complex decision-making . it is based on the dual-process theory of cognitive science, where two cognitive systems interact to form a whole reasoning process.
Approach:	They propose a unified reasoning framework that mirrors the modular reasoning process of humans . they decouple the representation module and the reasoning modules to capture different levels of cognition .
Outcome:	The proposed framework shows that humans can perform better in complex decision-making tasks.

Modeling News Interactions and Influence for Financial Market Prediction (2024.findings-emnlp)

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Mengyu Wang, Shay Cohen, Tiejun Ma

Sentiment Analysis

Challenge:	Existing studies often adopt a simplified approach by treating available news data holistically and investigating its overall effect on market outcomes, the nuanced information contained within individual news items is overlooked.
Approach:	They propose a market prediction model that integrates multi-modal information from both market data and news articles to capture the links between news and prices.
Outcome:	The proposed model outperforms existing market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively.

Improving Neural Machine Translation with Neural Syntactic Distance (N19-1)

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Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao

Multilinguality

Challenge:	Neural syntactic distance (NSD) is used to represent constituent trees using a sequence whose length is identical to the number of words in the sentence.
Approach:	They propose five strategies to improve NMT with explicit use of syntactic information . et al., 2014) propose a set of five strategies that incorporate syntastic information into the encoder and/or decoder of the baseline model.
Outcome:	The proposed strategies improve translation performance of the baseline model (+2.1 (En–Ja), +1.3 (Ja–En), +1.2 (En-Ch), and +1.0 (Ch–En) BLEU.

FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval (2025.findings-emnlp)

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Ying Li, Mengyu Wang, Miguel de Carvalho, Sotirios Sabanis, Tiejun Ma

Information Retrieval

Challenge:	FinGEAR provides a retrieval framework tailored to financial documents . standard retrieval-augmented generation models underuse financial disclosures .
Approach:	FinGEAR combines a finance lexicon for Item-level guidance and hierarchical indices for within-Item search.
Outcome:	FinGEAR improves accuracy and accuracy on 10-Ks with a FinQA dataset.

On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning (2022.naacl-main)

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Tingting Ma, Qianhui Wu, Zhiwei Yu, Tiejun Zhao, Chin-Yew Lin

Semantic Text Processing

Challenge:	Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem.
Approach:	They propose to modify a few-shot intent detection task to produce a non-trivially strong performance without further domain-specific adaptation.
Outcome:	The proposed model improves on the prototypical network variants with task-specific fine-tuning.

Dynamic Planning for LLM-based Graphical User Interface Automation (2024.findings-emnlp)

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Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen, Xinbei Ma, Muyun Yang, Tiejun Zhao, Min Zhang

Language Models

Challenge:	Existing approaches to planning for GUI tasks are limited due to long historical dialogues.
Approach:	They propose a novel approach to dynamic planning based on environmental feedback and execution history to guide action prediction in GUI tasks.
Outcome:	The proposed approach surpasses the strong GPT-4V baseline by +12.7% in accuracy.

CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition (2023.acl-long)

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Tingting Ma, Qianhui Wu, Huiqiang Jiang, Börje Karlsson, Tiejun Zhao, Chin-Yew Lin

Information Extraction & Text Mining

Challenge:	Existing approaches to named entity recognition (NER) are limited by the cost of labeling and labeling, especially for low-resource languages.
Approach:	They propose a model-collaboration-based denoising scheme that enables models trained on different data sources to collaboratively denoise pseudo labels used by each other.
Outcome:	The proposed framework achieves superior results on benchmark datasets and can generalize to distant languages.

Decomposed Meta-Learning for Few-Shot Named Entity Recognition (2022.findings-acl)

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Tingting Ma, Huiqiang Jiang, Qianhui Wu, Tiejun Zhao, Chin-Yew Lin

Language Models

Challenge:	Named entity recognition systems aim at recognizing unseen entity types based on a few labeled examples.
Approach:	They propose a decomposed meta-learning approach to solve few-shot span detection and few- shot entity typing problems by introducing a model-agnostic meta-loop algorithm.
Outcome:	The proposed approach achieves superior performance over prior methods on benchmarks.

Forest-Based Neural Machine Translation (P18-1)

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Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Tiejun Zhao, Eiichiro Sumita

Multilinguality

Challenge:	Compared with string-to-string systems, tree-based NMT methods use more syntactic information and can incorporate prior knowledge.
Approach:	They propose a tree-based neural machine translation method that translates a linearized packed forest under a simple sequence-to-sequence framework.
Outcome:	The proposed method outperforms tree-based approaches in the BLEU score of the proposed model.

One More Question is Enough, Expert Question Decomposition (EQD) Model for Domain Quantitative Reasoning (2025.findings-emnlp)

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Mengyu Wang, Sotirios Sabanis, Miguel de Carvalho, Shay B Cohen, Tiejun Ma

Responsible & Trustworthy NLP

Challenge:	Domain-specific quantitative reasoning remains a challenge for large language models . we propose an approach to balance domain knowledge with computational efficiency .
Approach:	They propose an approach to balance domain knowledge with computational efficiency . it uses a two-step fine-tuning framework and a reward function to measure sub-questions' effectiveness .
Outcome:	The proposed approach outperforms state-of-the-art domain-tuned models and advanced prompting strategies in the financial domain.

Issues with Entailment-based Zero-shot Text Classification (2021.acl-short)

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Tingting Ma, Jin-Ge Yao, Chin-Yew Lin, Tiejun Zhao

Information Extraction & Text Mining

Challenge:	Pre-trained BERT models with no fine-tuning can yield competitive performance against BERT fine- tuned for NLI.
Approach:	They propose to use any target label into a sentence of hypothesis and verify whether it could be entailed by the input.
Outcome:	The proposed models perform better than models fine-tuned for BERT, but the results are in general negative.