Spain

We describe the systems developed by the National Research Council Canada for the Uralic language identification shared task at the 2020 VarDial evaluation campaign. Although our official results were well below the baseline, we show in this poster that this was not due to the neural approach to language identification in general, but to a flaw in the function we used to sample data for training and evaluation purposes. Preliminary experiments conducted after the evaluation period suggest that our neural approach to language identification can achieve state-of-the-art results on this task, although further experimentation is required.

COLING 2020

Challenges in Neural Language Identification: NRC at VarDial 2020

language identification

natural language processing

deep learning

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

Analyzing a tweet can provide a general estimation of a tweet location while giving insight into the dialect of the user and other linguistic markers. Such linguistic attributes can be used to provide a regional approximation of tweet origins. In this paper, we present a neural regression model that can identify the linguistic intricacies of a tweet to predict the location of the user. The final model identifies the dialect embedded in the tweet and predicts the location of the tweet.

Geolocation of Tweets with a BiLSTM Regression Model

We participate in the FNS-Summarisation 2020 shared task to be held at FNP 2020 workshop at COLING 2020. Based on Determinantal Point Processes (DPPs), we build an extractive automatic financial summarisation system for the specific task. In this system, we first analyze the long report data to select the important narrative parts and generate an intermediate document. Next, we build the kernel Matrix L for the intermediate document, which represents the quality of its sentences. On the basis of L, we then can use the DPPs sampling algorithm to choose those sentences with high quality and diversity as the final summary sentences.

Extractive Financial Narrative Summarisation based on DPPs

The work proposes a combination of linguistic features with features derived from pre-trained BERT for training a supervised classifier for the task of Causality Detection. The Linguistic features help to inject knowledge about the semantic and syntactic structure of the input sentences. Experiments on the FinCausal Shared Task1 datasets indicate that the combination of Linguistic features with BERT improves overall performance for causality detection. The proposed system achieves a weighted average F1 score of 0.952 on the post-evaluation dataset.

A Knowledge Induced Neural Net for Causality Detection

This poster describes a system for causality extraction from financial documents submitted as part of the FinCausal 2020 Workshop. The proposed system achieved a weighted-averaged F1 score of more than 95% for the official blind test set during the post-evaluation phase and exact clauses match for 83% of the documents.

Span-based Causality Extraction for Financial Documents

Companies provide annual reports to their shareholders at the end of the financial year that describes their operations and financial conditions. The average length of these reports is 80, and it may extend up to 250 pages long. In this paper, we propose our methodology PoinT-5 (the combination of Pointer Network and T-5 (Test-to-text transfer Transformer) algorithms) that we used in the Financial Narrative Summarisation (FNS) 2020 task. The proposed method uses Pointer networks to extract important narrative sentences from the report, and then T-5 is used to paraphrase extracted sentences into a concise yet informative sentence. We evaluate our method using ROUGE-N (1,2), L,and SU4. The proposed method achieves the highest precision scores in all the metrics and highest F1 scores in ROUGE 1,and LCS and only solution to cross MUSE solution baseline in ROUGE-LCS metrics.


Pointer Network and T-5 based Financial Narrative
Summarisation


This paper describes the systems proposed to solve the FNS 2020 Shared Task on summarizing financial reports. We present an extractive approach that implements a statistical model comprised of different features that measure the relevance of the sentences using a combination of statistical and machine learning methods. 


Combining financial word embeddings and knowledge-based features for financial text summarization: UC3M-MC System at FNS-2020

In this poster, we describe the results of team LIORI at the FinCausal 2020 Shared task held as a part of the 1st Joint Workshop on Financial Narrative Processing and MultiLingual Financial Summarisation.

LIORI at FinCausal 2020, Tasks 1 & 2

This paper describes the approach we built for the Financial Document Causality Detection Shared Task (FinCausal-2020) Task 2: Cause and Effect Detection. Our approach is based on a multi-class classifier using BiLSTM with Graph Convolutional Neural Network (GCN) trained by minimizing the binary cross entropy loss. In our approach, we have not used any extra data source apart from combining the trial and practice dataset. We achieve weighted F1 score to 75.61 percent and are ranked at 7-th place.

Financial Document Causality Detection

The rapid growth of textual data published everyday on the web has prompted the need for automatic text summarization methodologies. In the financial domain, quoted companies are required to periodically publish a report in textual form.
Depending on the reader, those reports may contain redundant or irrelevant information. The proposed task aims at highlighting business-relevant information while analyzing the annual reports.
The proposed methodology exploit the advancements in the Natural Language Understanding field to create a fine-tuned architecture able to specialize the general knowledge towards the financial domain.

End-to-end Training For Financial Report Summarization

With the constantly growing amount of information, the need arises to automatically summarize this written information. One of the challenges in the summary is that it’s difficult to generalize. For example, summarizing a news article is very different from summarizing a financial earnings report. This paper reports an approach for summarizing financial texts, which are different from the documents from other domains at least in three parameters: length, structure, and format. Our
approach considers these parameters, it is adapted to hierarchical structure of sections, document length, and special “language”. The approach builds an hierarchical summary, visualized as a tree with summaries under different discourse topics. The approach was evaluated using extrinsic and intrinsic automated evaluations, which are reported in this paper. As all participants of the Financial Narrative Summarisation (FNS 2020) shared task, we used FNS2020 dataset for
evaluations.

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