Financial Document Causality Detection Shared Task (FinCausal 2020)
- URL: http://arxiv.org/abs/2012.02505v1
- Date: Fri, 4 Dec 2020 10:17:42 GMT
- Title: Financial Document Causality Detection Shared Task (FinCausal 2020)
- Abstract summary: This workshop is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020)
We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present the FinCausal 2020 Shared Task on Causality Detection in Financial
Documents and the associated FinCausal dataset, and discuss the participating
systems and results. Two sub-tasks are proposed: a binary classification task
(Task 1) and a relation extraction task (Task 2). A total of 16 teams submitted
runs across the two Tasks and 13 of them contributed with a system description
paper. This workshop is associated to the Joint Workshop on Financial Narrative
Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The
28th International Conference on Computational Linguistics (COLING'2020),
Barcelona, Spain on September 12, 2020.
Related papers
- Overview of FinMMEval 2026 Task 2: Multilingual Financial Short-Answer Question Answering [59.6753725406784]
FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence.<n>The final-test set contains 256 items, split evenly between easy and expert tiers.<n>The strongest systems are closely clustered, with the top four separated by less than one percentage point in ROUGE-1 F1.
arXiv Detail & Related papers (2026-07-22T07:54:17Z) - The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems [54.12165004393043]
FinMMEval 2026 offers three interconnected tasks that span financial understanding, reasoning, and decision-making.<n>The lab aims to promote the development of robust, transparent, and globally inclusive financial AI systems.
arXiv Detail & Related papers (2026-02-11T14:14:06Z) - GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection: AI vs. Human [71.42669028683741]
We present a shared task on binary machine generated text detection conducted as a part of the GenAI workshop at COLING 2025.
The task consists of two subtasks: Monolingual (English) and Multilingual.
We provide a comprehensive overview of the data, a summary of the results, detailed descriptions of the participating systems, and an in-depth analysis of submissions.
arXiv Detail & Related papers (2025-01-19T11:11:55Z) - Findings of the IWSLT 2024 Evaluation Campaign [102.7608597658451]
The paper reports on the shared tasks organized by the 21st IWSLT Conference.
The shared tasks address 7 scientific challenges in spoken language translation.
arXiv Detail & Related papers (2024-11-07T19:11:55Z) - Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications [90.67346776473241]
Large language models (LLMs) have advanced financial applications, yet they often lack sufficient financial knowledge and struggle with tasks involving multi-modal inputs like tables and time series data.
We introduce textitOpen-FinLLMs, a series of Financial LLMs that embed comprehensive financial knowledge into text, tables, and time-series data.
We also present FinLLaVA, a multimodal LLM trained with 1.43M image-text instructions to handle complex financial data types.
arXiv Detail & Related papers (2024-08-20T16:15:28Z) - 'Finance Wizard' at the FinLLM Challenge Task: Financial Text Summarization [0.0]
This paper documents our pipeline approach of fine-tuning a foundation model into a task-specific model for Financial Text Summarization.
Our model, FinLlama3_sum, yielded commendable results, securing the third position in its category with a ROUGE-1 score of 0.521.
arXiv Detail & Related papers (2024-08-07T13:31:44Z) - L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization [2.111699987679628]
FinLLM Challenge Task 2024 focused on two key areas: Task 1, financial text classification, and Task 2, financial text summarization.
We fine-tuned several large language models (LLMs) to optimize performance for each task.
Our models achieved third place for the financial classification task with an F1-score of 0.7543 and secured sixth place in the financial summarization task on the official test datasets.
arXiv Detail & Related papers (2024-08-06T08:25:49Z) - Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks [62.443665295250035]
We present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023)
In total, 32 competing teams register for the challenge, from which we received 11 successful submissions.
arXiv Detail & Related papers (2024-07-20T10:13:54Z) - FinBen: A Holistic Financial Benchmark for Large Language Models [75.09474986283394]
FinBen is the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks.
FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.
arXiv Detail & Related papers (2024-02-20T02:16:16Z) - PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark
for Finance [63.51545277822702]
PIXIU is a comprehensive framework including the first financial large language model (LLMs) based on fine-tuning LLaMA with instruction data.
We propose FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks.
We conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks.
arXiv Detail & Related papers (2023-06-08T14:20:29Z) - I4U System Description for NIST SRE'20 CTS Challenge [87.17861348484455]
This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge.
The I4U's submission was resulted from active collaboration among eight research teams.
The submission was based on the fusion of top performing sub-systems and sub-fusion systems contributed by individual teams.
arXiv Detail & Related papers (2022-11-02T13:04:27Z) - DSC-IITISM at FinCausal 2021: Combining POS tagging with Attention-based
Contextual Representations for Identifying Causal Relationships in Financial
Documents [0.0]
Causality detection has applications in information retrieval, event prediction, question answering, financial analysis, and market research.
In this study, we explore several methods to identify and extract cause-effect pairs in financial documents using transformers.
Our best methodology achieves an F1-Score of 0.9551, and an Exact Match Score of 0.8777 on the blind test.
arXiv Detail & Related papers (2021-10-31T13:09:19Z) - Data Processing and Annotation Schemes for FinCausal Shared Task [0.0]
This task is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020)
This document explains the annotation schemes used to label the data for the FinCausal Shared Task (Mariko et al., 2020)
arXiv Detail & Related papers (2020-12-04T09:58:47Z) - WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet
Lab Protocols [17.782052529098927]
This paper presents the results of the wet lab information extraction task at WNUT 2020.
We outline the task, data annotation process, corpus statistics, and provide a high-level overview of the participating systems for each sub task.
arXiv Detail & Related papers (2020-10-27T19:34:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.