Spain

We introduced the masked reasoner system that participated in 
SemEval-2020 Task 4: Commonsense Validation and Explanation. The system participated in the subtask B. We propose a novel method to fine-tune RoBERTa by masking the most important word in the statement. We believe that the confidence of the system in recovering that word is positively correlated to the score the masked language model assigns to the current statement-explanation pair. We evaluate the importance of each word using InferSent and do the masked fine-tuning on RoBERTa. Then we use the fine-tuned model to predict the most plausible explanation. Our system is fast in training and achieved 73.5% accuracy.

COLING 2020

Fine-Tuning RoBERTa for Commonsense Reasoning

commonsense reasoning

knowledge graph

**Welcome to ACL-IJCNLP 2021!**

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As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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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.

This paper describes the UCD-Net system entered for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We propose a novel method based on distance between temporally referenced lexical nodes in a semantic network constructed from a combination of the time specific corpora. We argue for the value of semantic networks as objects for transparent exploratory analysis and visualisation of lexical semantic change, and present an application of an
interactive semantic network exploration tool for interpreting specific aspects of subtle changes inconceptual meaning.

Temporal Referencing
with Semantic Network Distances

In this paper, we explore strategies to detect and evaluate counterfactual sentences. We describe our system for SemEval-2020 Task 5: Modeling Causal Reasoning in Language: Detecting Counterfactuals. We use a BERT base model for the classification task and build a hybrid BERT Multi-Layer Perceptron system to handle the sequence identification task. Our experiments show that while introducing syntactic and semantic features does little in improving the system in the classification task, using these types of features as cascaded linear inputs to fine-tune the sequence-delimiting ability of the model ensures it outperforms other similar-purpose complex systems like BiLSTM-CRF in the second task. Our system achieves an F1 score of 85.00% in Task 1 and 83.90% in Task 2.

Cascaded BERT Language Model for Counterfactual Statement Analysis

We attempt to demonstrate how to measure the relationships between a historical text and two other works of fiction written six centuries later when they have a similar set of characters with a social network framework. In order to achieve this, we annotated named-entities in the three texts in a semi-automatic way and made the annotation available for future research. We show that the surviving characters with a conserved pronunciation from the Nibelungenlied and from the Völsunga saga got more prominent roles than they had in Decem Libri Historium.

History to Myths: Social Network Analysis for Comparison of Stories over Time

We provide a comprehensive overview of existing systems for the computational generation of verbal humor in the form of jokes and short humorous texts. Considering linguistic humor the- ories, we analyze the systematic strengths and drawbacks of the different approaches. In addi- tion, we show how the systems have been evaluated so far and propose two evaluation criteria: humorousness and complexity. From our analysis of the field, we conclude new directions for the advancement of computational humor generation.

A Survey on Approaches to Computational Humor Generation

The quality of Optical Character Recognition (OCR) is a key factor in the digitisation of historical documents. OCR errors are a major obstacle for downstream tasks and have hindered advances in the usage of the digitised documents. In this paper we present a two-step approach to automatic OCR post-correction. The first component is responsible for detecting erroneous sequences in a set of OCRed texts, while the second is designed for correcting OCR errors in them. We show that applying the preceding detection model reduces both the character error rate (CER) compared to a simple one-step correction model and the amount of falsely changed correct characters.

A Two-Step Approach for Automatic OCR Post-Correction

In this paper we describe an approach for the computer-aided identification of Shakespearean intertextuality in a corpus of contemporary fiction. We present the Vectorian, which is a framework that implements different word embeddings and various NLP parameters. The Vectorian works like a search engine, i.e. a Shakespearean phrase can be entered as a query, the underlying collection of fiction books is then searched for the phrase and the passages that are likely to contain the phrase, either verbatim or as a paraphrase, are presented in a ranked results list. While the Vectorian can be used via a GUI, in which many different parameters can be set and combined manually, in this paper we present an ablation study that automatically evaluates different embedding and NLP parameter combinations against a ground truth. We investigate the behavior of different parameters during the evaluation and discuss how our results may be used for future studies on the detection of Shakespearean intertextuality.

"Shakespeare in the Vectorian Age" – An evaluation of different word embeddings and NLP parameters for the detection of Shakespeare quotes

We present Vital Records, a demonstrator based on deep-learning approaches to handwritten-text recognition, table processing and information extraction, which enables data from century-old documents to be parsed and analysed, making it possible to explore death records in space and time. This demonstrator provides a user interface for browsing and visualising data extracted from 80,000 handwritten pages of tabular data.

Vital Records: Uncover the past from historical handwritten records

Prose fiction typically consists of passages alternating between the narrator's telling of the story and the characters' direct speech in that story. Detecting direct speech is crucial for the downstream analysis of narrative structure, and may seem easy at first thanks to quotation marks. However, typographical conventions vary across languages, and as a result, almost all approaches to this problem have been monolingual. In contrast, the aim of this paper is to provide a multilingual method for identifying direct speech. To this end, we created a training corpus by using a set of heuristics to automatically find texts where quotation marks appear sufficiently consistently. We then removed the quotation marks and developed a sequence classifier based on multilingual-BERT which classifies each token as belonging to narration or speech. Crucially, by training the classifier with the quotation marks removed, it was forced to learn the linguistic characteristics of direct speech rather than the typography of quotation marks. The results in the zero-shot setting of the proposed model are comparable to the strong supervised baselines, indicating that this is a feasible approach.

Zero-shot cross-lingual identification of direct speech using distant supervision

This poster accompanies the publication of the EncycNet corpus, a novel XML/TEI annotated corpus of 22 historical German encyclopedias from the early 18th to early 20th century (over 50 million word tokens). While some previous encyclopedia projects have suffered from restrictive or unclear licensing, or paywalled or no longer functional web interfaces, we share the EncycNet corpus under the CC-BY license and long term archiving at Zenodo. This poster describes our suggested methodology for TEI annotation, namely how the TEI Lex-0 standard may be modified with additional guidelines for the annotation of historical encyclopedias. We suggest some possible use cases for the EncycNet corpus, including linguistic, historical, and literary research, and present our plan for future work, including the addition of further semantic annotation, alignment of encyclopedia entries based on semantic web (GermaNet and DBpedia), and the development of the corpus as a knowledge graph. As the digitization and annotation of historical encyclopedias is always moving forward, our methodological choices may inform similar projects. The EncycNet corpus is available at: http://dx.doi.org/10.5281/zenodo.4039569

Twenty-two Historical Encyclopedias Encoded in TEI: a New Resource for the Digital Humanities

The paper investigates the impact of using geometric deep learning models on the performance of a character name linking system. The neural models that contain graph convolutional layers are confronted with the models that include conventional fully connected layers. The evaluation is performed with respect to the perfect name boundaries obtained from the test set and in a more demanding end-to-end setting where the character name linking system is preceded by a named entity recognizer.


Fine-Tuning RoBERTa for Commonsense Reasoning

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Fine-Tuning RoBERTa for Commonsense Reasoning

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