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Despite recent achievements in natural language understanding, reasoning over commonsense knowledge still represents a big challenge to AI systems. As the name suggests, common sense is related to perception and as such, humans derive it from experience rather than from literary education. Recent works in the NLP and the computer vision field have made the effort of making such knowledge explicit using written language and visual inputs, respectively. Our premise is that the latter source fits better with the characteristics of commonsense acquisition. In this work, we explore to what extent the descriptions of real-world scenes are sufficient to learn common sense about different daily situations, drawing upon visual information to answer script knowledge questions.

COLING 2020

Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension

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

Evaluations of image description systems are typically domain-general: generated descriptions for the held-out test images are either compared to a set of reference descriptions (using automated metrics), or rated by human judges on one or more Likert scales (for fluency, overall quality, and other quality criteria). While useful, these evaluations do not tell us anything about the kinds of image descriptions that systems are able to produce. Or, phrased differently, these evaluations do not tell us anything about the cognitive capabilities of image description systems. This paper proposes a different kind of assessment, that is able to quantify the extent to which these systems are able to describe humans. This assessment is based on a manual characterization (a context-free grammar) of English entity labels in the PEOPLE domain, to determine the range of possible outputs. We examined 9 systems to see what kinds of labels they actually use. We found that these systems only use a small subset of at most 13 different kinds of modifiers (e.g. tall and short modify HEIGHT, sad and happy modify MOOD), but 27 kinds of modifiers are never used. Future research could study these semantic dimensions in more detail.

How do image description systems describe people? A targeted assessment of system competence in the PEOPLE-domain

Fine-tuning the multilingual BERT model on the multilingual emoji prediction task. The emojis are mapped to the basic emotions of Plutchik, and a case study is presented on tweets collected on the topic of the COVID-19 pandemic.

Multilingual Emoticon Prediction of Tweets about COVID-19

News editorials aim to shape the opinions of their readership and the general public on timely controversial issues. The impact of an editorial on the reader’s opinion does not only depend on its content and style, but also on the reader’s profile. Previous work has studied the effect of editorial style depending on general political ideologies (liberals vs. conservatives). In our work, we dig deeper into the persuasiveness of both content and style, exploring the role of the intensity of an ideology (lean vs. extreme) and the reader’s personality traits (agreeableness, conscientiousness, extraversion, neuroticism, and openness). Concretely, we train content- and style-based models on New York Times editorials for different ideology- and personality-specific groups. Our results suggest that particularly readers with extreme ideology and non “role model” personalities are impacted by style. We further analyze the importance of various text features with respect to the editorials’ impact, the readers’ profile, and the editorials’ geographical scope.

Persuasiveness of News Editorials depending on Ideology and Personality

As a contribution to personality detection in languages other than English, we rely on distant supervision to create Personal-ITY, a novel corpus of YouTube comments in Italian, where authors are labelled with personality traits. The traits are derived from one of the mainstream personality theories in psychology research, named MBTI. Using personality prediction experiments, we (i) study the task of personality prediction in itself on our corpus as well as on TWISTY, a Twitter dataset also annotated with MBTI labels; (ii) carry out an extensive, in-depth analysis of the features used by the classifier, and view them specifically under the light of the original theory that we used to create the corpus in the first place. We observe that no single model is best at personality detection, and that while some traits are easier than others to detect, and also to match back to theory, for other, less frequent traits the picture is much more blurred.

Matching Theory and Data with Personal-ITY: What a Corpus of Italian YouTube Comments Reveals About Personality

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.

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

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.

Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension

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How do image description systems describe people? A targeted assessment of system competence in the PEOPLE-domain

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Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension

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Next from COLING 2020

How do image description systems describe people? A targeted assessment of system competence in the PEOPLE-domain

Similar lecture

Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

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RESOURCES

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