Charese Smiley


2024

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Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing
Chung-Chi Chen | Xiaomo Liu | Udo Hahn | Armineh Nourbakhsh | Zhiqiang Ma | Charese Smiley | Veronique Hoste | Sanjiv Ranjan Das | Manling Li | Mohammad Ghassemi | Hen-Hsen Huang | Hiroya Takamura | Hsin-Hsi Chen
Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing

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Large Language Models as Financial Data Annotators: A Study on Effectiveness and Efficiency
Toyin D. Aguda | Suchetha Siddagangappa | Elena Kochkina | Simerjot Kaur | Dongsheng Wang | Charese Smiley
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Collecting labeled datasets in finance is challenging due to scarcity of domain experts and higher cost of employing them. While Large Language Models (LLMs) have demonstrated remarkable performance in data annotation tasks on general domain datasets, their effectiveness on domain specific datasets remains under-explored. To address this gap, we investigate the potential of LLMs as efficient data annotators for extracting relations in financial documents. We compare the annotations produced by three LLMs (GPT-4, PaLM 2, and MPT Instruct) against expert annotators and crowdworkers. We demonstrate that the current state-of-the-art LLMs can be sufficient alternatives to non-expert crowdworkers. We analyze models using various prompts and parameter settings and find that customizing the prompts for each relation group by providing specific examples belonging to those groups is paramount. Furthermore, we introduce a reliability index (LLM-RelIndex) used to identify outputs that may require expert attention. Finally, we perform an extensive time, cost and error analysis and provide recommendations for the collection and usage of automated annotations in domain-specific settings.

2022

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When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain
Raj Shah | Kunal Chawla | Dheeraj Eidnani | Agam Shah | Wendi Du | Sudheer Chava | Natraj Raman | Charese Smiley | Jiaao Chen | Diyi Yang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Pre-trained language models have shown impressive performance on a variety of tasks and domains. Previous research on financial language models usually employs a generic training scheme to train standard model architectures, without completely leveraging the richness of the financial data. We propose a novel domain specific Financial LANGuage model (FLANG) which uses financial keywords and phrases for better masking, together with span boundary objective and in-filing objective. Additionally, the evaluation benchmarks in the field have been limited. To this end, we contribute the Financial Language Understanding Evaluation (FLUE), an open-source comprehensive suite of benchmarks for the financial domain. These include new benchmarks across 5 NLP tasks in financial domain as well as common benchmarks used in the previous research. Experiments on these benchmarks suggest that our model outperforms those in prior literature on a variety of NLP tasks. Our models, code and benchmark data will be made publicly available on Github and Huggingface.

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ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
Zhiyu Chen | Shiyang Li | Charese Smiley | Zhiqiang Ma | Sameena Shah | William Yang Wang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The community is experiencing the shift of the challenge from how to model language to the imitation of complex reasoning abilities like human beings. In this work, we investigate the application domain of finance that involves real-world, complex numerical reasoning. We propose a new large-scale dataset, ConvFinQA, aiming to study the chain of numerical reasoning in conversational question answering. Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations. We conduct comprehensive experiments and analyses with both the neural symbolic methods and the prompting-based methods, to provide insights into the reasoning mechanisms of these two divisions. We believe our new dataset should serve as a valuable resource to push forward the exploration of real-world, complex reasoning tasks as the next research focus. Our dataset and code is publicly available at https://github.com/czyssrs/ConvFinQA.

2021

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FinQA: A Dataset of Numerical Reasoning over Financial Data
Zhiyu Chen | Wenhu Chen | Charese Smiley | Sameena Shah | Iana Borova | Dylan Langdon | Reema Moussa | Matt Beane | Ting-Hao Huang | Bryan Routledge | William Yang Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The sheer volume of financial statements makes it difficult for humans to access and analyze a business’s financials. Robust numerical reasoning likewise faces unique challenges in this domain. In this work, we focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations. To facilitate analytical progress, we propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. We also annotate the gold reasoning programs to ensure full explainability. We further introduce baselines and conduct comprehensive experiments in our dataset. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge and in complex multi-step numerical reasoning on that knowledge. Our dataset – the first of its kind – should therefore enable significant, new community research into complex application domains. The dataset and code are publicly available at https://github.com/czyssrs/FinQA.

2018

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The E2E NLG Challenge: A Tale of Two Systems
Charese Smiley | Elnaz Davoodi | Dezhao Song | Frank Schilder
Proceedings of the 11th International Conference on Natural Language Generation

This paper presents the two systems we entered into the 2017 E2E NLG Challenge: TemplGen, a templated-based system and SeqGen, a neural network-based system. Through the automatic evaluation, SeqGen achieved competitive results compared to the template-based approach and to other participating systems as well. In addition to the automatic evaluation, in this paper we present and discuss the human evaluation results of our two systems.

2017

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Say the Right Thing Right: Ethics Issues in Natural Language Generation Systems
Charese Smiley | Frank Schilder | Vassilis Plachouras | Jochen L. Leidner
Proceedings of the First ACL Workshop on Ethics in Natural Language Processing

We discuss the ethical implications of Natural Language Generation systems. We use one particular system as a case study to identify and classify issues, and we provide an ethics checklist, in the hope that future system designers may benefit from conducting their own ethics reviews based on our checklist.

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Native Language Identification using Phonetic Algorithms
Charese Smiley | Sandra Kübler
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

In this paper, we discuss the results of the IUCL system in the NLI Shared Task 2017. For our system, we explore a variety of phonetic algorithms to generate features for Native Language Identification. These features are contrasted with one of the most successful type of features in NLI, character n-grams. We find that although phonetic features do not perform as well as character n-grams alone, they do increase overall F1 score when used together with character n-grams.

2016

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When to Plummet and When to Soar: Corpus Based Verb Selection for Natural Language Generation
Charese Smiley | Vassilis Plachouras | Frank Schilder | Hiroko Bretz | Jochen Leidner | Dezhao Song
Proceedings of the 9th International Natural Language Generation conference

2015

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Natural Language Question Answering and Analytics for Diverse and Interlinked Datasets
Dezhao Song | Frank Schilder | Charese Smiley | Chris Brew
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations