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FinCausal-2020 is the shared task which focuses on the causality detection of factual data for financial analysis. The financial data facts don’t provide much explanation on the variability of these data. This paper aims to propose an efficient method to classify the data into one which is having any financial cause or not. Many models were used to classify the data, out of which SVM model gave an F-Score of 0.9435, BERT with specific fine-tuning achieved best results with F-Score of 0.9677.

COLING 2020

NITK NLP at FinCausal2020 Task 1 using BERT and Linear models

causality

bert

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

poster

The FinCausal 2020 shared task aims to detect causality on financial news and identify those parts of the causal sentences related to the underlying cause and effect. We apply ensemble-based and sequence tagging methods for identifying causality, and extracting causal subsequences. Our models yield promising results on both sub-tasks, with the prospect of further improvement given more time and computing resources. With respect to task 1, we achieved an F1 score of 0.9429
on the evaluation data, and a corresponding ranking of 12/14. For task 2, we were ranked 6/10, with an F1 score of 0.76 and an ExactMatch score of 0.1912.

Using Ensemble Methods and Sequence Tagging to
Detect Causality in Financial Documents

In order to provide an explanation of machine learning models, causality detection attracts lots of attention in the artificial intelligence research community. In this paper, we explore the cause-effect detection in financial news and propose an approach, which combines the BIO scheme with the Viterbi decoder for addressing this challenge. Our approach is ranked the first in the official run of cause-effect detection (Task 2) of the FinCausal-2020 shared task. We not only report the implementation details and ablation analysis in this paper but also publish our code
for academic usage.

Improving Causality Detection Using Viterbi Decoder

This poster describes our system developed for the sub-task 1 of the FinCausal shared task in the FNP-FNS workshop held in conjunction with COLING-2020. The system classifies whether a financial news text segment contains causality or not. To address this task, we fine-tune and ensemble the generic and domain-specific BERT language models pre-trained on financial text corpora. The task data is highly imbalanced with the majority \textit{non-causal} class; therefore, we train the models using strategies such as under-sampling, cost-sensitive learning, and data augmentation. Our best system achieves a weighted F1-score of $96.98$ securing $4^{th}$ position on the evaluation leaderboard. The code is available at \url{https://github.com/sarthakTUM/fincausal}

Mixture of BERTs for Causal Sentence Identification in Financial Texts

This paper introduces our efforts at the FinCasual shared task for modeling causality in financial utterances.
Our approach uses the commonly and successfully applied strategy of fine-tuning a transformer-based language model with a twist, i.e. we modified the training and inference mechanism such that our model produces multiple predictions for the same instance.
By designing such a model that returns k>1 predictions at the same time, we not only obtain a more resource efficient training (as opposed to fine-tuning some pre-trained language model k independent times), but our results indicate that we are also capable of obtaining comparable or even better evaluation scores that way.
We compare multiple strategies for combining the k predictions of our model.
Our submissions got ranked third on both subtasks of the shared task.

Modeling causality in financial texts using multi-headed transformers

We explore a section-based extractive summarization method tailored to the structure of financial reports: our best system parses the report Table of Contents (ToC), splits the report into narrative sections based on the ToC, and applies a BERT-based classifier to each section to determine whether it should be included in the summary. Our best system ranks 4th, 1st, 2nd, and 17th on the Rouge-1, Rouge-2, Rouge-SU4, and Rouge-L official metrics, respectively.

SUMSUM@FNS-2020 Shared Task

Identifying semantic relationships has become increasingly important for multiple downstream applications like Question Answering and Event Prediction. We tackle the problem of causal relationship extraction from financial news using the FinCausal 2020 dataset in two parts: 1) Detect the presence of causal relations. 2) Extract segments corresponding to cause and effect from news snippets.

Causal Extraction System

The paper describes the work that the team submitted to FinCausal 2020 Shared Task. This work is associated with the first sub-task of identifying causality in sentences. The various models used in the experiments tried to obtain a latent space representation for each of the sentences. Linear regression was performed on these representations to classify whether the sentence is causal or not. The experiments have shown BERT (Large) performed the best, giving a F1 score of 0.958, in the task of detecting the causality of sentences in financial texts and reports. The class imbalance was dealt with a modified loss function to give a better metric score for the evaluation.

Causality detection using Sentence Embeddings in Financial Reports

We present our contributions for the FinTOC'2 Shared Tasks: Table of Content (ToC) extraction in English documents and French documents. 
For ToC Extraction, we propose an approach that combines information from multiple sources: the table of contents, the wording of the document, and lexical domain knowledge.
For the title detection part, we compare surface features to character-based features on various training configurations. We show that title detection results are very sensitive to the kind of training dataset used.

Title Detection and Structure Extraction

In this paper, we present three methods developed by the Sorbonne Team for the NADI shared task on Arabic Dialect Identification for tweets.
The first and the second method use respectively a machine learning model based on a Voting Classifier with words and character level features and a deep learning model at the word level. The third method uses only character-level features. We explored different text representation such as TF-IDF (first model) and word embeddings (second model). The Voting Classifier was the most powerful prediction model, achieving the best macro-average F1 score of 18.8% and an accuracy of 36.54\% on the official test.
Our model ranked 9 on the challenge and in conclusion we propose some ideas to improve its results.

Voting Classifier vs Deep learning method in Arabic Dialect Identification

Arabic dialect detection is an important subdomain for studying various indigenous features of different societies. Limitation of the labeled data-set for training models drives us for alternative techniques. We introduce a semi-supervised learning approach for generating pseudo-labeled data for fine-tuning neural network models. This strategy improves performance when compared to training classifiers in a supervised fashion and also produces better results with reduced training time.

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