ymoslem/acl-abstracts huggingface dataset:
Greek within the Global FrameNet Initiative: Challenges and Conclusions so far (2020)

Large coverage lexical resources that bear deep linguistic information have always been considered useful for many natural language processing (NLP) applications including Machine Translation (MT). In this respect, Frame-based resources have been developed for many languages following Frame Semantics and the Berkeley FrameNet project. However, to a great extent, all those efforts have been kept fragmented. Consequentially, the Global FrameNet initiative has been conceived of as a joint effort to bring together FrameNets in different languages. The proposed paper is aimed at describing ongoing work towards developing the Greek (EL) counterpart of the Global FrameNet and our efforts to contribute to the Shared Annotation Task. In the paper, we will elaborate on the annotation methodology employed, the current status and progress made so far, as well as the problems raised during annotation.
Using Verb Frames for Text Difficulty Assessment (2020)

This paper presents the first investigation on using semantic frames to assess text difficulty. Based on Mandarin VerbNet, a verbal semantic database that adopts a frame-based approach, we examine usage patterns of ten verbs in a corpus of graded Chinese texts. We identify a number of characteristics in texts at advanced grades: more frequent use of non-core frame elements; more frequent omission of some core frame elements; increased preference for noun phrases rather than clauses as verb arguments; and more frequent metaphoric usage. These characteristics can potentially be useful for automatic prediction of text readability.
Deriving a PropBank Corpus from Parallel FrameNet and UD Corpora (2020)

We propose an approach for generating an accurate and consistent PropBank-annotated corpus, given a FrameNet-annotated corpus which has an underlying dependency annotation layer, namely, a parallel Universal Dependencies (UD) treebank. The PropBank annotation layer of such a multi-layer corpus can be semi-automatically derived from the existing FrameNet and UD annotation layers, by providing a mapping configuration from lexical units in [a non-English language] FrameNet to [English language] PropBank predicates, and a mapping configuration from FrameNet frame elements to PropBank semantic arguments for the given pair of a FrameNet frame and a PropBank predicate. The latter mapping generally depends on the underlying UD syntactic relations. To demonstrate our approach, we use Latvian FrameNet, annotated on top of Latvian UD Treebank, for generating Latvian PropBank in compliance with the Universal Propositions approach.
Building the Emirati Arabic FrameNet (2020)

The Emirati Arabic FrameNet (EAFN) project aims to initiate a FrameNet for Emirati Arabic, utilizing the Emirati Arabic Corpus. The goal is to create a resource comparable to the initial stages of the Berkeley FrameNet. The project is divided into manual and automatic tracks, based on the predominant techniques being used to collect frames in each track. Work on the EAFN is progressing, and we here report on initial results for annotations and evaluation. The EAFN project aims to provide a general semantic resource for the Arabic language, sure to be of interest to researchers from general linguistics to natural language processing. As we report here, the EAFN is well on target for the first release of data in the coming year.
Exploring Crosslinguistic Frame Alignment (2020)

The FrameNet (FN) project at the International Computer Science Institute in Berkeley (ICSI), which documents the core vocabulary of contemporary English, was the first lexical resource based on Fillmore`s theory of Frame Semantics. Berkeley FrameNet has inspired related projects in roughly a dozen other languages, which have evolved somewhat independently; the current Multilingual FrameNet project (MLFN) is an attempt to find alignments between all of them. The alignment problem is complicated by the fact that these projects have adhered to the Berkeley FrameNet model to varying degrees, and they were also founded at different times, when different versions of the Berkeley FrameNet data were available. We describe several new methods for finding relations of similarity between semantic frames across languages. We will demonstrate ViToXF, a new tool which provides interactive visualizations of these cross-lingual relations, between frames, lexical units, and frame elements, based on resources such as multilingual dictionaries and on shared distributional vector spaces, making clear the strengths and weaknesses of different alignment methods.
Building Multilingual Specialized Resources Based on FrameNet: Application to the Field of the Environment (2020)

The methodology developed within the FrameNet project is being used to compile resources in an increasing number of specialized fields of knowledge. The methodology along with the theoretical principles on which it is based, i.e. Frame Semantics, are especially appealing as they allow domain-specific resources to account for the conceptual background of specialized knowledge and to explain the linguistic properties of terms against this background. This paper presents a methodology for building a multilingual resource that accounts for terms of the environment. After listing some lexical and conceptual differences that need to be managed in such a resource, we explain how the FrameNet methodology is adapted for describing terms in different languages. We first applied our methodology to French and then extended it to English. Extensions to Spanish, Portuguese and Chinese were made more recently. Up to now, we have defined 190 frames: 112 frames are new; 38 are used as such; and 40 are slightly different (a different number of obligatory participants; a significant alternation, etc.) when compared to Berkeley FrameNet.
The Financial Narrative Summarisation Shared Task (FNS 2020) (2020)

This paper presents the results and findings of the Financial Narrative Summarisation shared task (FNS 2020) on summarising UK annual reports. The shared task was organised as part of the 1st Financial Narrative Processing and Financial Narrative Summarisation Workshop (FNP-FNS 2020). The shared task included one main task which is the use of either abstractive or extractive summarisation methodologies and techniques to automatically summarise UK financial annual reports. FNS summarisation shared task is the first to target financial annual reports. The data for the shared task was created and collected from publicly available UK annual reports published by firms listed on the London Stock Exchange (LSE). A total number of 24 systems from 9 different teams participated in the shared task. In addition we had 2 baseline summarisers and additional 2 topline summarisers to help evaluate and compare against the results of the participants.
The Financial Document Structure Extraction Shared task (FinToc 2020) (2020)

This paper presents the FinTOC-2020 Shared Task on structure extraction from financial documents, its participants results and their findings. This shared task was organized as part of The 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING`2020). This shared task aimed to stimulate research in systems for extracting table-of-contents (TOC) from investment documents (such as financial prospectuses) by detecting the document titles and organizing them hierarchically into a TOC. For the second edition of this shared task, two subtasks were presented to the participants: one with English documents and the other one with French documents.
The Financial Document Causality Detection Shared Task (FinCausal 2020) (2020)

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.
LangResearchLab\_NC at FinCausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection (2020)

Identifying causal relationships in a text is essential for achieving comprehensive natural language understanding. The present work proposes a combination of features derived from pre-trained BERT with linguistic features 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.
GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents (2020)

This document describes a system for causality extraction from financial documents submitted as part of the FinCausal 2020 Workshop. The main contribution of this paper is a description of the robust post-processing used to detect the number of cause and effect clauses in a document and extract them. The proposed system achieved a weighted-average 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.
LIORI at the FinCausal 2020 Shared task (2020)

In this paper, 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. The shared task consisted of two subtasks: classifying whether a sentence contains any causality and labelling phrases that indicate causes and consequences. Our team ranked 1st in the first subtask and 4th in the second one. We used Transformer-based models with joint-task learning and their ensembles.
JDD @ FinCausal 2020, Task 2: Financial Document Causality Detection (2020)

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.
UPB at FinCausal-2020, Tasks 1 \& 2: Causality Analysis in Financial Documents using Pretrained Language Models (2020)

Financial causality detection is centered on identifying connections between different assets from financial news in order to improve trading strategies. FinCausal 2020 - Causality Identification in Financial Documents {--} is a competition targeting to boost results in financial causality by obtaining an explanation of how different individual events or chain of events interact and generate subsequent events in a financial environment. The competition is divided into two tasks: (a) a binary classification task for determining whether sentences are causal or not, and (b) a sequence labeling task aimed at identifying elements related to cause and effect. Various Transformer-based language models were fine-tuned for the first task and we obtained the second place in the competition with an F1-score of 97.55{\%} using an ensemble of five such language models. Subsequently, a BERT model was fine-tuned for the second task and a Conditional Random Field model was used on top of the generated language features; the system managed to identify the cause and effect relationships with an F1-score of 73.10{\%}. We open-sourced the code and made it available at: \url{https://github.com/avramandrei/FinCausal2020}.
NITK NLP at FinCausal-2020 Task 1 Using BERT and Linear models. (2020)

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.
Fraunhofer IAIS at FinCausal 2020, Tasks 1 \& 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents (2020)

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.
NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder (2020)

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.
FiNLP at FinCausal 2020 Task 1: Mixture of BERTs for Causal Sentence Identification in Financial Texts (2020)

This paper 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 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 4th position on the evaluation leaderboard. The code is available at \url{https://github.com/sarthakTUM/fincausal}
ProsperAMnet at FinCausal 2020, Task 1 \& 2: Modeling causality in financial texts using multi-headed transformers (2020)

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{\ensuremath{>}}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.
ISIKUN at the FinCausal 2020: Linguistically informed Machine-learning Approach for Causality Identification in Financial Documents (2020)

This paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of the FinCausal-2020 Shared Task. The first sub-task (Task-1) consists of the binary classification of the given sentences as causal meaningful (1) or causal meaningless (0). Our approach for the Task-1 includes applying linear support vector machines after transforming the input sentences into vector representations using term frequency-inverse document frequency scheme with 3-grams. The second sub-task (Task-2) consists of the identification of the cause-effect relations in the sentences, which are detected as causal meaningful. Our approach for the Task-2 is a CRF-based model which uses linguistically informed features. For the Task-1, the obtained results show that there is a small difference between the proposed approach based on linear support vector machines (F-score 94{\%}) , which requires less time compared to the BERT-based baseline (F-score 95{\%}). For the Task-2, although a minor modifications such as the learning algorithm type and the feature representations are made in the conditional random fields based baseline (F-score 52{\%}), we have obtained better results (F-score 60{\%}). The source codes for the both tasks are available online (\url{https://github.com/ozenirgokberk/FinCausal2020.git/}).
Domino at FinCausal 2020, Task 1 and 2: Causal Extraction System (2020)

Automatic identification of cause-effect relationships from data is a challenging but important problem in artificial intelligence. Identifying semantic relationships has become increasingly important for multiple downstream applications like Question Answering, Information Retrieval and Event Prediction. In this work, we tackle the problem of causal relationship extraction from financial news using the FinCausal 2020 dataset. We tackle two tasks - 1) Detecting the presence of causal relationships and 2) Extracting segments corresponding to cause and effect from news snippets. We propose Transformer based sequence and token classification models with post-processing rules which achieve an F1 score of 96.12 and 79.60 on Tasks 1 and 2 respectively.
IITkgp at FinCausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports (2020)

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.
Extractive Financial Narrative Summarisation based on DPPs (2020)

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.
PoinT-5: Pointer Network and T-5 based Financial Narrative Summarisation (2020)

Companies provide annual reports to their shareholders at the end of the financial year that de-scribes their operations and financial conditions. The average length of these reports is 80, andit may extend up to 250 pages long. In this paper, we propose our methodology PoinT-5 (thecombination of Pointer Network and T-5 (Test-to-text transfer Transformer) algorithms) that weused in the Financial Narrative Summarisation (FNS) 2020 task. The proposed method usesPointer networks to extract important narrative sentences from the report, and then T-5 is used toparaphrase extracted sentences into a concise yet informative sentence. We evaluate our methodusing Rouge-N (1,2), L, and SU4. The proposed method achieves the highest precision scores inall the metrics and highest F1 scores in three out of four evaluation metrics that are Rouge 1, 2,and LCS and only solution to cross MUSE solution baseline in Rouge-LCS metrics.
Combining financial word embeddings and knowledge-based features for financial text summarization UC3M-MC System at FNS-2020 (2020)

This paper describes the systems proposed by HULAT research group from Universidad Carlos III de Madrid (UC3M) and MeaningCloud (MC) company to solve the FNS 2020 Shared Task on summarizing financial reports. We present a narrative 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. The key to the model`s performance is its accurate representation of the text, since the word embeddings used by the model have been trained with the summaries of the training dataset and therefore capture the most salient information from the reports. The systems' code can be found at \url{https://github.com/jaimebaldeon/FNS-2020}.
End-to-end Training For Financial Report Summarization (2020)

Quoted companies are requested to periodically publish financial reports in textual form. The annual financial reports typically include detailed financial and business information, thus giving relevant insights into company outlooks. However, a manual exploration of these financial reports could be very time consuming since most of the available information can be deemed as non-informative or redundant by expert readers. Hence, an increasing research interest has been devoted to automatically extracting domain-specific summaries, which include only the most relevant information. This paper describes the SumTO system architecture, which addresses the Shared Task of the Financial Narrative Summarisation (FNS) 2020 contest. The main task objective is to automatically extract the most informative, domain-specific textual content from financial, English-written documents. The aim is to create a summary of each company report covering all the business-relevant key points. To address the above-mentioned goal, we propose an end-to-end training method relying on Deep NLP techniques. The idea behind the system is to exploit the syntactic overlap between input sentences and ground-truth summaries to fine-tune pre-trained BERT embedding models, thus making such models tailored to the specific context. The achieved results confirm the effectiveness of the proposed method, especially when the goal is to select relatively long text snippets.