Spain

hierarchical planning

minecraft

instruction giving

planning

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 2020

When generating technical instructions, it is often convenient to describe complex objects in the world at different levels of abstraction. A novice user might need an object explained piece by piece, while for an expert, talking about the complex object (e.g. a wall or railing) directly may be more succinct and efficient. We show how to generate building instructions at different levels of abstraction in Minecraft. We introduce the use of hierarchical planning to this end, a method from AI planning which can capture the structure of complex objects neatly. A crowdsourcing evaluation shows that the choice of abstraction level matters to users, and that an abstraction strategy which balances low-level and high-level object descriptions compares favorably to ones which don’t.

Generating Instructions at Different Levels of Abstraction

technical paper

Contextual Argument Component Classification for Class Discussions

A Deep Metric Learning Method for Biomedical Passage Retrieval

SLICE: Supersense-based Lightweight Interpretable Contextual Embeddings

What Meaning-Form Correlation Has to Compose With

Exploring End-to-End Differentiable Natural Logic Modeling

Out-of-Task Training for Dialog State Tracking Models

LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization

Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment
in Natural Language Processing

On the Ability of Recurrent Neural Networks to Recognize Hierarchical Languages

TransQuest: Translation Quality Estimation with Cross-lingual Transformers

https://d3smihljt9218e.cloudfront.net/transcript/4349/subtitle/d8c7b39bbd3f8b5c0aa0c75946b11e77.vtt

https://d3smihljt9218e.cloudfront.net/transcript/5031/subtitle/a21aad3148309152b7ac502325cb81e6.vtt

English (automatic)

English

Generating Instructions at Different Levels of Abstraction

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Contextual Argument Component Classification for Class Discussions

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