Jiaxin Liu
2024
Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives
Jiaxin Liu
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Yi Yang
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Kar Yan Tam
Findings of the Association for Computational Linguistics: NAACL 2024
In this paper, we introduce the Financial-STS task, a financial domain-specific NLP task designed to measure the nuanced semantic similarity between pairs of financial narratives. These narratives originate from the financial statements of the same company but correspond to different periods, such as year-over-year comparisons. Measuring the subtle semantic differences between these paired narratives enables market stakeholders to gauge changes over time in the company’s financial and operational situations, which is critical for financial decision-making. We find that existing pretrained embedding models and LLM embeddings fall short in discerning these subtle financial narrative shifts. To address this gap, we propose an LLM-augmented pipeline specifically designed for the Financial-STS task. Evaluation on a human-annotated dataset demonstrates that our proposed method outperforms existing methods trained on classic STS tasks and generic LLM embeddings.
GeoAgent: To Empower LLMs using Geospatial Tools for Address Standardization
Chenghua Huang
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Shisong Chen
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Zhixu Li
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Jianfeng Qu
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Yanghua Xiao
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Jiaxin Liu
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Zhigang Chen
Findings of the Association for Computational Linguistics ACL 2024
This paper presents a novel solution to tackle the challenges that posed by the abundance of non-standard addresses, which input by users in modern applications such as navigation maps, ride-hailing apps, food delivery platforms, and logistics services. These manually entered addresses often contain irregularities, such as missing information, spelling errors, colloquial descriptions, and directional offsets, which hinder address-related tasks like address matching and linking. To tackle these challenges, we propose GeoAgent, a new framework comprising two main components: a large language model (LLM) and a suite of geographical tools. By harnessing the semantic understanding capabilities of the LLM and integrating specific geospatial tools, GeoAgent incorporates spatial knowledge into address texts and achieves efficient address standardization. Further, to verify the effectiveness and practicality of our approach, we construct a comprehensive dataset of complex non-standard addresses, which fills the gaps in existing datasets and proves invaluable for training and evaluating the performance of address standardization models in this community. Experimental results demonstrate the efficacy of GeoAgent, showcasing substantial improvements in the performance of address-related models across various downstream tasks.
Explicit Attribute Extraction in e-Commerce Search
Robyn Loughnane
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Jiaxin Liu
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Zhilin Chen
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Zhiqi Wang
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Joseph Giroux
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Tianchuan Du
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Benjamin Schroeder
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Weiyi Sun
Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024
This paper presents a model architecture and training pipeline for attribute value extraction from search queries. The model uses weak labels generated from customer interactions to train a transformer-based NER model. A two-stage normalization process is then applied to deal with the problem of a large label space: first, the model output is normalized onto common generic attribute values, then it is mapped onto a larger range of actual product attribute values. This approach lets us successfully apply a transformer-based NER model to the extraction of a broad range of attribute values in a real-time production environment for e-commerce applications, contrary to previous research. In an online test, we demonstrate business value by integrating the model into a system for semantic product retrieval and ranking.
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Co-authors
- Yi Yang 1
- Kar Yan Tam 1
- Chenghua Huang 1
- Shisong Chen 1
- Zhixu Li 1
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