Spain

&quot;Keyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, digital libraries store millions of scientific articles online, covering a wide range of topics. While a significant portion of these articles contain keyphrases provided by their authors, most other articles lack such kind of annotations. Therefore, to effectively utilize these large amounts of unlabeled articles, we propose a simple and efficient joint learning approach based on the idea of self-distillation. Experimental results show that our approach consistently improves the performance of baseline models for keyphrase extraction. Furthermore, our best models outperform previous methods for the task, achieving new state-of-the-art results on two public benchmarks: Inspec and SemEval-2017.
&quot;
KEY WORDS: keyphrase extraction, joint learning, semi-supervised learning

COLING 2020

A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents

keyphrase extraction

semi-supervised learning

joint learning

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.

"Keyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, digital libraries store millions of scientific articles online, covering a wide range of topics. While a significant portion of these articles contain keyphrases provided by their authors, most other articles lack such kind of annotations. Therefore, to effectively utilize these large amounts of unlabeled articles, we propose a simple and efficient joint learning approach based on the idea of self-distillation. Experimental results show that our approach consistently improves the performance of baseline models for keyphrase extraction. Furthermore, our best models outperform previous methods for the task, achieving new state-of-the-art results on two public benchmarks: Inspec and SemEval-2017.
"
KEY WORDS: keyphrase extraction, joint learning, semi-supervised learning

poster

The AMR (Abstract Meaning Representation) formalism for representing
meaning of natural language sentences puts emphasis on predicate-argument structure and was not designed to deal with scope and quantifiers. By extending AMR with indices for contexts and formulating constraints on these contexts, a formalism is derived that makes correct predictions for inferences involving negation and bound variables. The attractive core predicate-argument structure of AMR is preserved. The resulting framework is similar to the meaning representations of Discourse Representation Theory employed in the Parallel Meaning Bank.

Separating Argument Structure from Logical Structure in AMR

Ethics in the Vision and Language of Artificial Intelligence

Armenian is a language with significant variation and unevenly distributed NLP resources for different varieties. An attempt is made to process an RNN model for morphological annotation on the basis of different Armenian data (provided or not with morphologically annotated corpora), and to compare the annotation results of RNN and rule-based models. Different tests were carried out to evaluate the reuse of an unspecialized model of lemmatization and POS-tagging for under-resourced language varieties. The research focused on three dialects and further extended to Western Armenian with a mean accuracy of 94,00 % in lemmatization and 97,02% in POS-tagging, as well as a possible reusability of models to cover different other Armenian varieties. Interestingly, the comparison of an RNN model trained on Eastern Armenian with the Eastern Armenian National Corpus rule-based model applied to Western Armenian showed an enhancement of 19% in parsing. This model covers 88,79% of a short heterogeneous dataset in Western Armenian, and could be a baseline for a massive corpus annotation in that standard. It is argued that an RNN-based model can be a valid alternative to a rule-based one giving consideration to such factors as time-consumption, reusability for different varieties of a target language and significant qualitative results in morphological annotation.

Recycling and Comparing Morphological Annotation Models for Armenian Diachronic-Variational Corpus Processing

Meta-Information Guided Meta-Learning for Few-Shot Relation Classification

Detecting funniness level using traditional learning with sentence embeddings

Context-Aware Text Normalisation for Historical Dialects

In this talk, I describe recent efforts to bring language-guided navigation agents from the Vision-and-Language Navigation task to robotic platforms.

Exploring the Sim-2-Real Gap in Vision-and-Language Navigation

"Language reflects demographics, as people of different demographic groups systematically use language differently. That holds also for gender. Gender-speficic language affects NLP tools, usually because it is not sufficiently modeled. Instead, models assume all language is the same. If they are ignored, these linguistic differences lead to uneven performance and even discrimination. If we instead start paying attention to WHO is talking, and not just WHAT is said, they give us the opportunity to model language in all its diversity, open new applications, and improve both performance and fairness.
In this talk, I will first show how gender representation is reflected in syntax, and then how this affects various NLP tasks. Lack of awareness of gendered differences mean that NLP models perform differently for different genders in part of speech tagging and parsing, and also affects the style of machine translations and sentence completions in BERT and GPT2."

Sampling, Syntax, and Sentence Completions.The (Overlooked?) Impact of Gender on NLP Tools

We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018) coreference system on Dutch datasets of two domains: literary novels and news/Wikipedia text. The results provide insight into the relative strengths of data-driven and knowledge-driven systems, as well as the influence of domain, document length, and annotation schemes. The neural system performs best on news/Wikipedia text, while the rule-based system performs best on literature. The neural system shows weaknesses with limited training data and long documents, while the rule-based system is affected by annotation differences. The code and models used in this paper are available at https://github.com/andreasvc/crac2020

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Separating Argument Structure from Logical Structure in AMR

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Next from COLING 2020

Separating Argument Structure from Logical Structure in AMR

Similar lecture

KeyGames: A Game Theoretic approach to Automatic Keyphrase Extraction

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

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