Spain

The presentation described the system briefly, followed by a demonstration of functionalities.

COLING 2020

Annobot: Platform for Annotating and Creating Datasets through Conversation with a Chatbot

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.

technical paper

Developing a platform that analyzes the content of curricula can help identify their shortcomings and whether they are tailored to specific desired outcomes. In this paper, we present a system to analyze Arabic curricula and provide insights into their content. It allows users to explore word presence, surface-forms used, as well as contrasting statistics between different countries from which the curricula were selected. Also, it provides a facility to grade text in reference to given grade-level and gives users feedback about the complexity or difficulty of words used in a text.

Arabic Curriculum Analysis

E-mail is a communication tool widely used by people of all ages on the Internet today, often in business and formal situations, especially in Japan. Moreover, Japanese E-mail communication has a set of specific rules taught using specialized guidebooks. E-mail literacy education for many Japanese students is typically provided in a traditional, yet inefficient lecture-based way. We propose a system to support Japanese students in writing E-mails to superiors (teachers, job hunting representatives, etc.). We firstly make an investigation into the importance of formal E-mails in Japan, and what is needed to successfully write a formal E-mail. Next, we develop the system in accordance with those rules. Finally, we evaluated the system twofold. The results, although performed on a small number of samples, were generally positive, and clearly indicated additional ways to improve the system.

Epistolary Education in 21st Century: A System to Support Composition of E-mails by Students to Superiors in Japanese

For the study of language change, historical data must be enriched with linguistic annotations. Since the manual creation of annotations requires a lot of effort, automating the process with NLP methods would be convenient. But the required amounts of training data are usually not available for historical data. This talk presents a study that investigates whether models trained on modern newspaper text can be used to automatically identify topological fields, i.e. syntactic structures, in different modern and historical German texts.

Automatic Topological Field Identification in (Historical) German Texts

Entity recognition provides semantic access to ancient materials in the Digital Humanities: it exposes people and places of interest in texts that cannot be read exhaustively, facilitates linking resources and can provide a window into text contents, even for texts with no translations. In this paper we present entity recognition for Coptic, the language of Hellenistic era Egypt. We evaluate NLP approaches to the task and lay out difficulties in applying them to a low-resource, morphologically complex language. We present solutions for named and non-named nested entity recognition and semi-automatic entity linking to Wikipedia, relying on robust dependency parsing, feature-based CRF models, and hand-crafted knowledge base resources, enabling high accuracy NER with orders of magnitude less data than those used for high resource languages. The results suggest avenues for research on other languages in similar settings.

Exhaustive Entity Recognition for Coptic - Challenges and Solutions

We investigate the use of Iconclass in the context of neural machine translation for NL↔EN artwork titles. Iconclass is a widely used iconographic classification system used in the cultural heritage domain to describe and retrieve subjects represented in the visual arts. The resource contains keywords and definitions to encode the presence of objects, people, events and ideas depicted in artworks, such as paintings. We propose a simple concatenation approach that im- proves the quality of automatically generated title translations for artworks, by leveraging textual information extracted from Iconclass. Our results demonstrate that a neural machine translation system is able to exploit this metadata to boost the translation performance of artwork titles. This technology enables interesting applications of machine learning in resource-scarce domains in the cultural sector.

Neural Machine Translation of Artwork Titles Using Iconclass Codes

Downstream effects of biased training data have become a major concern of the NLP community. How this may impact the automated curation and annotation of cultural heritage material is currently not well known. In this work, we create an experimental framework to measure the effects of different types of stylistic and social bias within training data for the purposes of literary classification, as one important subclass of cultural material. Because historical collections are often sparsely annotated, much like our knowledge of history is incomplete, researchers often cannot know the underlying distributions of different document types and their various sub-classes. This means that bias is likely to be an intrinsic feature of training data when it comes to cultural heritage material. Our aim in this study is to investigate which classification methods may help mitigate the effects of different types of bias within curated samples of training data. We find that machine learning techniques such as BERT or SVM are robust against reproducing certain kinds of social and stylistic bias within our test data, except in the most extreme cases. We hope that this work will spur further research into the potential effects of bias within training data for other cultural heritage material beyond the study of literature.

Measuring the Effects of Bias in Training Data for Literary Classification

Grammatical Error Correction (GEC) is the task of correcting different types of errors in written texts. To manage this task, large amounts of annotated data that contain erroneous sentences are required. This data, however, is usually annotated according to each annotator’s standards, making it difficult to manage multiple sets of data at the same time. The recently introduced Error Annotation Toolkit (ERRANT) tackled this problem by presenting a way to automatically annotate data that contain grammatical errors, while also providing a standardisation for annotation. ERRANT extracts the errors and classifies them into error types, in the form of an edit that can be used in the creation of GEC systems, as well as for grammatical error analysis. However, we observe that certain errors are falsely or ambiguously classified. This could obstruct any qualitative or quantitative grammatical error type analysis, as the results would be inaccurate. In this work, we use a sample of the FCE coprus (Yannakoudakis et al., 2011) for secondary error type annotation and we show that up to 39% of the annotations of the most frequent type should be re-classified. Our corrections will be publicly released, so that they can serve as the starting point of a broader, collaborative, ongoing correction process.

ERRANT: Assessing and Improving Grammatical Error Type
Classification

An increasing amount of historic data is now available in digital (text) formats.
This gives quantitative researchers an opportunity to use distant reading techniques, as opposed to traditional close reading, in order to analyse larger quantities of historic data. Distant reading allows researchers to view overall patterns within the data and reduce researcher bias. One such data set that has recently been transcribed is a collection of over 500 Australian World War I (WW1) diaries held by the State Library of New South Wales. Here we apply distant reading techniques to this corpus to understand what soldiers wrote about and how they felt over the course of the war. Extracting dates accurately is important as it allows us to perform our analysis over time, however, it is very challenging due to the variety of date formats and abbreviations diarists use. But with that data, topic modelling and sentiment analysis can then be applied to show trends, for instance, that despite the horrors of war, Australians in WW1 primarily wrote about their everyday routines and experiences. Our results detail some of the challenges likely to be encountered by quantitative researchers intending to analyse historical texts, and provide some approaches to these issues.

Life still goes on: Analysing Australian WW1 Diaries through Distant Reading

Creating a story is difficult. Professional writers often experience a writer's block. Thus, providing automatic support to writers is crucial but also challenging. Recently, in the field of generating and understanding stories, story completion (SC) has been proposed as a method for generating missing parts of an incomplete story. Despite this method's usefulness in providing creative support, its applicability is currently limited because it requires the user to have prior knowledge of the missing part of a story. Writers do not always know which part of their writing is flawed. To overcome this problem, we propose a novel approach called “missing position prediction (MPP).” Given an incomplete story, we aim to predict the position of the missing part. We also propose a novel method for MPP and SC. We first conduct an experiment focusing on MPP, and our analysis shows that highly accurate predictions can be obtained when the missing part of a story is the beginning or the end. This suggests that if a story has a specific beginning or end, they play significant roles. We conduct an experiment on SC using MPP, and our proposed method demonstrates promising results.

Finding and Generating a Missing Part for Story Completion

Previous approaches to NLP tasks on online patient forums have been limited to single posts as units, thereby neglecting the overarching conversational structure. In this paper we explore the benefit of exploiting conversational context for filtering posts relevant to a specific medical topic. We experiment with two approaches to add conversational context to a BERT model: a sequential CRF layer and manually engineered features. Although neither approach can outperform the F1 score of the BERT baseline, we find that adding a sequential layer improves precision for all target classes whereas adding a non-sequential layer with manually engineered features leads to a higher recall for two out of three target classes. Thus, depending on the end goal, conversation-aware modelling may be beneficial for identifying relevant messages. We hope our findings encourage other researchers in this domain to move beyond studying messages in isolation towards more discourse-based data collection and classification.

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