Spain

&quot;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.&quot;

COLING 2020

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

"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."

technical paper

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.

This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew. UCCA builds on extensive typological work and supports rapid annotation. The tutorial will provide a detailed introduction to the UCCA annotation guidelines, design philosophy and the available resources; and a comparison to other meaning representations. It will also survey the existing parsing work, including the findings of three recent shared tasks, in SemEval and CoNLL, that addressed UCCA parsing. Finally, the tutorial will present recent applications and extensions to the scheme, demonstrating its value for natural language processing in a range of languages and domains.

Parsing, Evaluation and Applications

While UCCA structures and categories are intentionally coarse, the scheme has a multi-layered architecture, which allows for refinement using additional layers, which serve as "modules" of semantic distinctions. We will give an overview of the recently proposed extensions (to support semantic roles, coreference, and implicit argument disambiguation). We will also situate UCCA in the meaning representation research landscape, comparing its design with widely-used formalisms such as AMR and UD.

Extension Layers and Comparison to Other Formalisms

This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew. UCCA builds on extensive typological work and supports rapid annotation. The tutorial will provide a detailed introduction to the UCCA annotation guidelines, design philosophy and the available resources; and a comparison to other meaning representations. It will also survey the existing parsing work, including the findings of three recent shared tasks, in SemEval and CoNLL, that addressed UCCA parsing. Finally, the tutorial will present recent applications and extensions to the scheme, demonstrating its value for natural language processing in a range of languages and domains.


Part 1.1 - A Bird's Eye View

Part 1.2 - A Bird's Eye View

Part 1.3 - A Bird's Eye View

Part 2 - Annotation of English

Part 3 - Annotated Corpora and UCCA-App

Large Scale Author Obfuscation Using Siamese Variational Auto-Encoder: The SiamAO System

This paper describes the SemEval-2020 shared task "Assessing Humor in Edited News Headlines." The task's dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version. To date, this task is the most popular shared computational humor task, attracting 48 teams for the first subtask and 31 teams for the second.

Assessing Humor in Edited News Headlines

Humans learn to understand speech from weak and noisy supervision: they manage to extract structure and meaning from speech by simply being exposed to utterances situated and grounded in their daily sensory experience. Emulating this remarkable skill has been the goal of numerous studies; however researchers have often used severely simplified settings where either the language input or the extralinguistic sensory input, or both, are small-scale and symbolically represented. I present a series of studies on modelling visually grounded language understanding. Using variations of recurrent neural networks to model the temporal nature of spoken language, we examine how form and meaning-based linguistic knowledge emerges from the input signal.

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

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

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Next from COLING 2020

Parsing, Evaluation and Applications

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads