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The computational treatment of temporal relations is based on the work of Allen, who establishes 13 different types, and Freksa, who designs a cognitive procedure to manage them. Freksa&#39;s notation is not widely used because, although it has cognitive and expressive advantages, it is too complex from the computational perspective. This paper proposes a system for the annotation and management of temporal relations that combines the richness and expressiveness of Freksa&#39;s approach with the simplicity of Allen&#39;s notation. Our method is summarized in the application of bounding relations, thanks to which it is possible to obtain the temporary representation of complete neighborhoods capable of representing vague temporal relations such as those that can be frequently found in a text. Such advantages are obtained without the need to greatly increase the complexity of the labeling process since the markup language is almost the same as TimeML, to which only a second temporary &quot;relType&quot; type label relationship is added. Our experiments show that the temporal relationships that present vagueness are in fact much more common than those in which a single relationship can be established precisely. For these reasons, our new labeling system achieves a more agreeable representation of temporal relations.

COLING 2020

Temporal Relations Annotation and Extrapolation Based on Semi-intervals and Bounding Relations

bounding relations

timeml

temporal relations

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.

The computational treatment of temporal relations is based on the work of Allen, who establishes 13 different types, and Freksa, who designs a cognitive procedure to manage them. Freksa's notation is not widely used because, although it has cognitive and expressive advantages, it is too complex from the computational perspective. This paper proposes a system for the annotation and management of temporal relations that combines the richness and expressiveness of Freksa's approach with the simplicity of Allen's notation. Our method is summarized in the application of bounding relations, thanks to which it is possible to obtain the temporary representation of complete neighborhoods capable of representing vague temporal relations such as those that can be frequently found in a text. Such advantages are obtained without the need to greatly increase the complexity of the labeling process since the markup language is almost the same as TimeML, to which only a second temporary "relType" type label relationship is added. Our experiments show that the temporal relationships that present vagueness are in fact much more common than those in which a single relationship can be established precisely. For these reasons, our new labeling system achieves a more agreeable representation of temporal relations.

poster

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Visual Question Answering (VQA) remains algorithmically challenging while it is effortless for humans. Humans combine visual observations with general and common sense knowledge to answer a question about a given image. In this paper, we address the problem of incorporating general knowledge into VQA models while leveraging the visual information. We propose a model that captures the interactions between objects in a visual scene and entities in an external knowledge source. Our model is a graph-based approach that combines scene graphs with concept graphs, which learns a question-adaptive graph representation of related knowledge in-stances. We use Graph Attention Networks to set higher importance to key knowledge instances that are mostly relevant to each question. We exploit ConceptNet as the source of general knowledge and evaluate the performance of our model on the challenging OK-VQA dataset.

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We probe the layers in multilingual BERT (mBERT) for phylogenetic and geographic language signals across 100 languages and compute language distances based on the mBERT representations. We 1) employ the language distances to infer and evaluate language trees, finding that they are close to the reference family tree in terms of quartet tree distance, 2) perform distance matrix regression analysis, finding that the language distances can be best explained by phylogenetic and worst by structural factors and 3) present a novel measure for measuring diachronic meaning stability (based on cross-lingual representation variability) which correlates significantly with published ranked lists based on linguistic approaches. Our results contribute to the nascent field of typological interpretability of cross-lingual text representations.

Probing Multilingual BERT for Genetic and Typological Signals

Mining commonsense knowledge from corpora suffers from reporting bias, over-representing the rare at the expense of the trivial (Gordon and Van Durme, 2013). We study to what extent pre-trained language models overcome this issue. We find that while their generalization capacity allows them to better estimate the plausibility of frequent but unspoken of actions, outcomes, and properties, they also tend to overestimate that of the very rare, amplifying the bias that already exists in their training corpus.

Do Neural Language Models Overcome Reporting Bias?

Narrow specialized comparable corpora are often small in size. This particularity makes it difficult to build efficient models to acquire translation equivalents, especially for less frequent and rare words. One way to overcome this issue is to enrich the specialized corpora with out-of-domain resources. Although some recent studies have shown improvements using data augmentation, the enrichment method was roughly conducted by adding out-of-domain data with no particular attention given to how to enrich words and how to do it optimally. In this paper, we contrast several data selection techniques to improve bilingual lexicon induction from specialized comparable corpora. We first apply two well-established data selection techniques often used in machine translation that is: Tf-Idf and cross entropy. Then, we propose to exploit BERT for data selection. Overall, all the proposed techniques improve the quality of the extracted bilingual lexicons by a large margin. The best performing model is the cross entropy, obtaining a gain of about 4 points in MAP while decreasing computation time by a factor of 10.

Data Selection for Bilingual Lexicon Induction from Specialized Comparable Corpora

This work presents a method of word sense clustering that differentiates homonyms and merge homophones, taking Japanese as an example, where orthographical variation causes problem for language processing. It uses contextualised embeddings (BERT) to cluster tokens into distinct sense groups, and we use these groups to normalise synonymous instances to a single representative form. We see the benefit of this normalisation in language model, as well as in transliteration.

Homonym normalisation by word sense clustering: a case in Japanese

Offensive and abusive language is a pressing problem on social media platforms. In this work, we propose a method for transforming offensive comments, statements containing profanity or offensive language, into non-offensive ones. We design a Retrieve, Generate, and Edit unsupervised style transfer pipeline to redact the offensive comments in a word-restricted manner while maintaining a high level of fluency and preserving the content of the original text. We extensively evaluate our method's performance and compare it to previous style transfer models using both automatic metrics and human evaluations. Experimental results show that our method outperforms other models on human evaluations and is the only approach that consistently performs well on all automatic evaluation metrics.

An Unsupervised Style Transfer Framework for Profanity Redaction

The Orthogonal Procrustes Analysis (PA) algorithm can be applied to find the optimal orthogonal matrix for word translation. This solution restricts the expressiveness of the translation model which may result in sub-optimal translations. We propose a natural extension of the PA algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matrices.

A Locally Linear Procedure for Word Translation

Abstractive summarization at controllable lengths is a challenging task in natural language processing. It is even more challenging for domains where limited training data is available or scenarios in which the length of the summary is not known beforehand. At the same time, when it comes to trusting machine-generated summaries, explaining how a summary was constructed in human-understandable terms may be critical. We propose Multi-level Summarizer (MLS), a supervised method to construct abstractive summaries of a text document at controllable lengths. The key enabler of our method is an interpretable multi-headed attention mechanism that computes attention distribution over an input document using an array of timestep independent semantic kernels. Each kernel optimizes a human-interpretable syntactic or semantic property. Exhaustive experiments on two low-resource datasets in the English language show that MLS outperforms strong baselines by up to 14.70% in the METEOR score. Human evaluation of the summaries also suggests that they capture the key concepts of the document at various length-budgets.

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Next from COLING 2020

Topic-driven Ensemble for Online Advertising Generation

Similar lecture

Semantic Role Labeling with Heterogeneous Syntactic Knowledge

Stay up to date with the latest Underline news!

PRESENTATIONS

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COMPANY

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