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In the last decade, the field of Neural Language Modelling has witnessed enormous changes, with the development of novel models through the use of Transformer architectures. However, even these models struggle to model long sequences due to memory constraints and increasing computational complexity. Coreference annotations over the training data can provide context far beyond the modelling limitations of such language models. In this paper we present an extension over the Transformer-block architecture used in neural language models, specifically in GPT2, in order to incorporate entity annotations during training. Our model, GPT2E, extends the Transformer layers architecture of GPT2 to Entity-Transformers, an architecture designed to handle coreference information when present. To that end, we achieve richer representations for entity mentions, with insignificant training cost. We show the comparative model performance between GPT2 and GPT2E in terms of Perplexity on the CoNLL 2012 and LAMBADA datasets as well as the key differences in the entity representations and their effects in downstream tasks such as Named Entity Recognition. Furthermore, our approach can be adopted by the majority of Transformer-based language models.

COLING 2020

E.T.: Entity-Transformers. Coreference augmented Neural Language Model for richer mention representations via Entity-Transformer blocks

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.

technical paper

While it has been claimed that anaphora or coreference resolution plays an important role in opinion mining, it is not clear to what extent coreference resolution actually boosts performance, if at all. In this paper, we investigate the potential added value of coreference resolution for the aspect-based sentiment analysis of restaurant reviews in two languages, English and Dutch. We focus on the task of aspect category classification and investigate whether including coreference
information prior to classification to resolve implicit aspect mentions is beneficial. Because coreference resolution is not a solved task in NLP, we rely on both automatically-derived and gold-standard coreference relations, allowing us to investigate the true upper bound. By training a classifier on a combination of lexical and semantic features, we show that resolving the coreferential relations prior to classification is beneficial in a joint optimization setup. However, this is only the case when relying on gold-standard relations and the result is more outspoken for English than for Dutch. When validating the optimal models, however, we found that only
the Dutch pipeline is able to achieve a satisfying performance on a held-out test set and does so regardless of whether coreference information was included.

It's absolutely divine! Can fine-grained sentiment analysis benefit from coreference resolution?

Pro-drop languages such as Arabic, Chinese, Italian or Japanese allow morphologically null but referential arguments in certain syntactic positions, called anaphoric zero-pronouns. Much NLP work on anaphoric zero-pronouns (AZP) is based on gold mentions, but models for their identification are a fundamental prerequisite for their resolution in real-life applications. Such identification requires complex language understanding and knowledge of real-world entities. Transfer learning models, such as BERT, have recently shown to learn surface, syntactic, and semantic information,which can be very useful in recognizing AZPs. We propose a BERT-based multilingual model for AZP identification from predicted zero pronoun positions, and evaluate it on the Arabic and Chinese portions of OntoNotes 5.0. As far as we know, this is the first neural network model of AZP identification for Arabic; and our approach outperforms the state-of-the-art for Chinese. Experiment results suggest that BERT implicitly encode information about AZPs through their surrounding context.

Anaphoric Zero Pronoun Identification: A Multilingual Approach

This work addresses coreference resolution in Abstract Meaning Representation (AMR) graphs, a popular formalism for semantic parsing. We evaluate several current coreference resolution techniques on a recently published AMR coreference corpus, establishing baselines for future work. We also demonstrate that coreference resolution can improve the accuracy of a state-of-the-art semantic parser on this corpus.

Predicting Coreference in Abstract Meaning Representations

This article introduces TwiConv, an English coreference-annotated corpus of microblog conversations from Twitter. We describe the corpus compilation process and the annotation scheme, and release the corpus publicly, along with this paper. We manually annotated nominal coreference in 1756 tweets arranged in 185 conversation threads. The annotation achieves satisfactory annotation agreement results. We also present a new method for mapping the tweet contents with distributed stand-off annotations, which can easily be adapted to different annotation tasks.

TwiConv: A Coreference-annotated Corpus of Twitter Conversations

Lexical semantics and world knowledge are crucial for interpreting
bridging anaphora. Yet, existing computational methods for acquiring
and injecting this type of information into bridging resolution
systems suffer important limitations. Based on explicit querying
of external knowledge bases, earlier approaches are computationally
expensive (hence, hardly scalable) and they map the data to be processed
into high-dimensional spaces (careful handling of the curse of 
dimensionality and overfitting has to be in order). In this work, 
we take a different and principled approach which naturally addresses
these issues. Specifically, we convert the external knowledge source (in
this case, WordNet) into a graph, and learn embeddings of the graph
nodes of low dimension to capture the crucial features of the graph topology and, at the same time, rich semantic information. Once properly identified from the mention text spans, these low dimensional graph node embeddings are combined with distributional text-based embeddings to provide 
enhanced mention representations. We illustrate the effectiveness of
our approach by evaluating it on commonly used datasets, namely ISNotes
and BASHI. Our enhanced mention representations yield
significant accuracy improvements on both datasets when compared to
different standalone text-based mention representations.

Integrating knowledge graph embeddings to improve mention representation for bridging anaphora resolution

Shell nouns (SNs) are abstract nouns like fact, issue, and decision, which are capable of referring to non-nominal antecedents, much like anaphoric pronouns. The talk takes a look at a special use case of certain nouns like "issue" and "decision" that seem to be prone to refer to globally available discourse topics instead of local antecedents, which has important implications for the treatment of SNs as a whole in theoretical and automatic approaches.

Reference to Discourse Topics: Introducing “Global” Shell Nouns

Learning to detect entity mentions without us- ing syntactic information can be useful for integration and joint optimization with other tasks. However, it is common to have par- tially annotated data for this problem. Here, we investigate two approaches to deal with partial annotation of mentions: weighted loss and soft-target classification. We also propose two neural mention detection approaches: a sequence tagging, and an exhaustive search. We evaluate our methods with coreference res- olution as a downstream task, using multitask learning. The results show that the recall and F1 score improve for all methods.

Partially-supervised Mention Detection

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.

Neural Coreference Resolution for Arabic

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.

E.T.: Entity-Transformers. Coreference augmented Neural Language Model for richer mention representations via Entity-Transformer blocks

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E.T.: Entity-Transformers. Coreference augmented Neural Language Model for richer mention representations via Entity-Transformer blocks

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Next from COLING 2020

It's absolutely divine! Can fine-grained sentiment analysis benefit from coreference resolution?

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Reference to Discourse Topics: Introducing “Global” Shell Nouns

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