Spain

No neural coreference resolver for Arabic exists, in fact we are not aware of any learning-based coreference resolver for Arabic since (Björkelund and Kuhn, 2014). In this paper, we introduce a coreference resolution system for Arabic based on Lee et al’s end-to-end architecture combined with the Arabic version of bert and an external mention detector. As far as we know, this is the first neural coreference resolution system aimed specifically to Arabic, and it substantially outperforms the existing state-of-the-art on OntoNotes 5.0 with a gain of 15.2 points conll F1.
We also discuss the current limitations of the task for Arabic and possible approaches that can tackle these challenges.

COLING 2020

Neural Coreference Resolution for Arabic

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

In this paper we describe our attempt to increase the amount of information that can be retrievedthrough active learning sessions compared to previous approaches. We optimise the annotator’slabelling process using active learning in the context of coreference resolution. Using simulatedactive learning experiments, we suggest three adjustments to ensure the labelling time is spentas efficiently as possible. All three adjustments provide more information to the machine learnerthan the baseline, though a large impact on the F1 score over time is not observed. Compared toprevious models, we report a marginal F1 improvement on the final coreference models trainedusing for two out of the three approaches tested when applied to the English OntoNotes 2012Coreference Resolution data. Our best-performing model achieves 58.01 F1, an increase of 0.93F1 over the baseline model.

Enhanced Labelling in Active Learning for Coreference Resolution

Many people live-tweet televised events like Presidential debates and popular TV-shows and discuss people or characters in the event. Naturally, many tweets make pronominal reference to these people/characters. We propose an algorithm for resolving personal pronouns that make reference to people involved in an event, in tweet streams collected during the event.

Resolving Pronouns in Twitter Streams: Context can Help!

We present a study focusing on variation of coreferential devices in English original TED talks and news texts and their German translations. Using exploratory techniques we contemplate a diverse set of coreference devices as features which we assume indicate language-specific and register-based variation as well as potential translation strategies. Our findings reflect differences on both dimensions with stronger variation along the lines of register than between languages. By exposing interactions between text type and cross-linguistic variation, they can also inform multilingual NLP applications, especially machine translation.

Coreference Strategies in English-German Translation

We introduce the IMS contribution to the Surface Realization Shared Task 2020. Our current system achieves substantial improvement over the state-of-the-art system from last year, mainly due to a better token representation and a better linearizer, as well as a simple ensembling approach. We also experiment with data augmentation, which brings some additional performance gain. The system is available at \url{https://github.com/EggplantElf/IMSurReal}.

IMSurReal Too: IMS at the Surface Realization Shared Task 2020

We present a system for mapping Universal Dependency structures to raw text which learns to
restore word order by training an Interpreted Regular Tree Grammar (IRTG) that establishes
a mapping between string and graph operations. The reinflection step is handled by a standard
sequence-to-sequence architecture with a biLSTM encoder and an LSTM decoder with attention.
We modify our 2019 system (Kovács et al., 2019) with a new grammar induction mechanism
that allows IRTG rules to operate on lemmata in addition to part-of-speech tags and ensures that
each word and its dependents are reordered using the most specific set of learned patterns. We
also introduce a hierarchical approach to word order restoration that independently determines
the word order of each clause in a sentence before arranging them with respect to the main
clause, thereby improving overall readability and also making the IRTG parsing task tractable.
We participated in the 2020 Surface Realization Shared task, subtrack T1a (shallow, closed).
Human evaluation shows we achieve significant improvements on two of the three out-of-domain
datasets compared to the 2019 system we modified. Both components of our system are available
on GitHub under an MIT license.

BME-TUW at SR’20: Lexical grammar induction for surface realization

This paper describes the submission by the NILC Computational Linguistics research group of the University of São Paulo/Brazil to the English Track 2 (closed sub-track) at the Surface Realisation Shared Task 2020. The success of the current pre-trained models like BERT or GPT-2 in several tasks is well-known, however, this is not the case for data-to-text generation tasks and just recently some initiatives focused on it. This way, we explore how a pre-trained model (GPT-2) performs on the UD-to-text generation task. In general, the achieved results were poor, but there are some interesting ideas to explore. Among the learned lessons we may note that it is necessary to study strategies to represent UD inputs and to introduce structural knowledge into these pre-trained models.

NILC at SR'20: Exploring Pre-Trained Models in Surface Realisation

In the context of Natural Language Generation, surface realization is the task of generating the linear form of a text following a given grammar. Surface realization models usually consist of a cascade of complex sub-modules, either rule-based or neural network-based, each responsible for a specific sub-task. In this work, we show that a single encoder-decoder language model can be used in an end-to-end fashion for all sub-tasks of surface realization. The model is designed based on the BART language model that receives a linear representation of unordered and non-inflected tokens in a sentence along with their corresponding Universal Dependency information and produces the linear sequence of inflected tokens along with the missing words. The model was evaluated on the shallow and deep tracks of the 2020 Surface Realization Shared Task (SR'20) using both human and automatic evaluation. The results indicate that despite its simplicity, our model achieves competitive results among all participants in the shared task.

Surface Realization Using Pretrained Language Models

Gender bias has been identified in many models for Natural Language Processing, stemming from implicit biases in the text corpora used to train the models. Such corpora are too large to closely analyze for biased or stereotypical content. Thus, we argue for a combination of quantitative and qualitative methods, where the quantitative part produces a view of the data of a size suitable for qualitative analysis. We investigate the usefulness of semi-supervised topic modeling for the detection and analysis of gender bias in three corpora (mainstream news articles in English and Swedish, and LGBTQ+ web content in English). We compare differences in topic models for three gender categories (masculine, feminine, and nonbinary or neutral) in each corpus. We find that in all corpora, genders are treated differently and that these differences tend to correspond to hegemonic ideas of gender.

Semi-Supervised Topic Modeling for Gender Bias Discovery in English and Swedish

There is a growing collection of work analyzing and mitigating societal biases in language understanding, generation, and retrieval tasks, though examining biases in creative tasks remains underexplored. Creative language applications are meant for direct interaction with users, so it is important to quantify and mitigate societal biases in these applications. We introduce a novel study on a pipeline to mitigate societal biases when retrieving next verse suggestions in a poetry composition system. Our results suggest that data augmentation through sentiment style transfer has potential for mitigating societal biases.

Investigating Societal Biases in a Poetry Composition System

Misrepresentation of certain communities in datasets is causing big disruptions in artificial intelligence applications. In this presentation, we review the related work on this field, explain the methodology that has been followed and present the results obtained.

Downloads

Next from COLING 2020

Enhanced Labelling in Active Learning for Coreference Resolution

Similar lecture

TwiConv: A Coreference-annotated Corpus of Twitter Conversations

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from COLING 2020

Enhanced Labelling in Active Learning for Coreference Resolution

Similar lecture

TwiConv: A Coreference-annotated Corpus of Twitter Conversations

Downloads