Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts
100 Things You Always Wanted to Know about Semantics & Pragmatics But Were Afraid to Ask (P18-5)
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| Challenge: | This tutorial aims to provide information about semantics and pragmatics, as understood in linguistics, in a way that’s accessible to and useful for NLP practitioners with minimal (or even no) prior training in linguistics. |
| Approach: | This tutorial presents a selection of useful information about semantics and pragmatics, as understood in linguistics. |
| Outcome: | The aim of the tutorial is to present useful information about semantics and pragmatics, as understood in linguistics, in a way that’s accessible to and useful for NLP practitioners with minimal (or even no) prior training in linguistry. |
Neural Approaches to Conversational AI (P18-5)
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| Challenge: | This tutorial examines neural approaches to conversational AI that have been developed in the last few years. |
| Approach: | This tutorial presents a review of state-of-the-art neural approaches to conversational AI . they group conversational systems into question answering agents, task-oriented dialogue agents and social bots . |
| Outcome: | The present tutorial examines state-of-the-art approaches to conversational AI . it draws the connection between neural approaches and traditional symbolic approaches . |
Variational Inference and Deep Generative Models (P18-5)
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| Challenge: | Unsupervised and semi-supervised learning has been addressed scarcely in NLP . this tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
| Approach: | This tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
| Outcome: | This tutorial provides an introduction to variational inference followed by an example-driven discussion of how to use variational methods for training DGMs. |
Connecting Language and Vision to Actions (P18-5)
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| Challenge: | Recent advances in language and vision have made incredible progress in describing images and interacting with visual content in a physical or embodied environment. |
| Approach: | This tutorial will provide an overview of the growing number of multimodal tasks and datasets that combine textual and visual understanding. |
| Outcome: | This tutorial will review the state-of-the-art approaches to selected tasks such as image captioning, visual question answering and visual dialog. |
Beyond Multiword Expressions: Processing Idioms and Metaphors (P18-5)
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| Challenge: | idioms and metaphors processing is a rapidly growing area in NLP, says dr. s. robertson . idiomatic idiomas are characteristic to all areas of human activity and to all types of discourse. |
| Approach: | This tutorial will provide attendees with a clear notion of idioms and metaphors . it will provide them with computational models of linguistic characteristics and methods . |
| Outcome: | This tutorial aims to provide attendees with a clear notion of the linguistic characteristics of idioms and metaphors . it outlines how to model idiomatic idiomes and their processing and what resources are available to support their use . |
Neural Semantic Parsing (P18-5)
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| Challenge: | Semantic parsing is the study of translating natural language utterances into machine-executable programs. |
| Approach: | They will describe the various approaches researchers have taken to translate natural language into a formal language . they will also discuss why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations. |
| Outcome: | This paper will describe the various approaches researchers have taken to translate natural language into a formal language. |
Deep Reinforcement Learning for NLP (P18-5)
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| Challenge: | Many natural language processing tasks can be formulated as deep reinforcement learning (DRL) problems. |
| Approach: | This tutorial provides an introduction to the foundations of deep reinforcement learning . it describes recent advances in designing deep reinforcement for NLP . |
| Outcome: | This tutorial provides an introduction to the foundations of deep reinforcement learning and some practical solutions for NLP tasks. |
Multi-lingual Entity Discovery and Linking (P18-5)
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| Challenge: | This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task. |
| Approach: | This tutorial will review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
| Outcome: | The aim of this tutorial is to review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |