Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
Machine Reasoning: Technology, Dilemma and Future (2020.emnlp-tutorials)
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| Challenge: | Machine reasoning is a field of research that aims to build interpretable AI systems . Symbolic reasoning methods represent knowledge using symbolic logic, perform inference . probabilistic reasoning methods combine probability and symbolic logic into a unified model . |
| Approach: | This tutorial introduces machine reasoning frameworks and aims to define them . it will show how they can be used to build interpretable AI systems . |
| Outcome: | This tutorial aims to show how machine reasoning frameworks perform in real-world scenarios. |
Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era (2020.emnlp-tutorials)
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| Challenge: | social media has made it easy for everyone to share and spread information online. |
| Approach: | a tutorial will offer an overview of the broad and emerging research area of disinformation . it will focus on the latest developments and research directions . |
| Outcome: | The tutorial will offer an overview of the broad and emerging research area of disinformation . it will focus on the latest developments and research directions . |
Interpreting Predictions of NLP Models (2020.emnlp-tutorials)
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| Challenge: | This tutorial will provide a background on interpretation techniques for neural NLP models. |
| Approach: | This tutorial will provide a background on interpretation techniques for NLP models . it will examine saliency maps, input perturbations, adversarial attacks and influence functions . |
| Outcome: | This tutorial will provide a background on interpretation techniques . examples-specific interpretations include saliency maps, input perturbations, adversarial attacks, influence functions . |
High Performance Natural Language Processing (2020.emnlp-tutorials)
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| Challenge: | a tutorial on scaling natural language processing will recapitulate the state-of-the-art in the field . |
| Approach: | This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective. |
| Outcome: | This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective. |
Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks (2020.emnlp-tutorials)
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| Challenge: | In this tutorial, we discuss the cutting-edge research results and existing challenges related to spatial language understanding including semantic annotations, existing corpora, symbolic and sub-symbolic representations, qualitative spatial reasoning, spatial common sense, deep and structured learning models. |
| Approach: | This tutorial presents cutting-edge research results and current challenges related to spatial language understanding including semantic annotations, existing corpora, symbolic and sub-symbolic representations, qualitative spatial reasoning, spatial common sense, deep and structured learning models. |
| Outcome: | This paper reviews the cutting-edge research results and current challenges related to spatial language understanding including semantic annotations, existing corpora, symbolic and sub-symbolic representations, qualitative spatial reasoning, spatial common sense, deep and structured learning models. |
Simultaneous Translation (2020.emnlp-tutorials)
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| Challenge: | Simultaneous translation is a problem that has long been considered one of the hardest problems in AI . this tutorial will provide a deep understanding of the history and the recent advances in simultaneous translation. |
| Approach: | This tutorial will examine the design and evaluation of policies for simultaneous translation . it will provide an overview of the history and recent advances in simultaneous translation. |
| Outcome: | This tutorial will examine the design and evaluation of policies for simultaneous translation . |
The Amazing World of Neural Language Generation (2020.emnlp-tutorials)
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| Challenge: | Recent years have seen a paradigm shift in neural text generation due to advances in deep contextual language modeling and transfer learning. |
| Approach: | They will discuss how and why NLG models succeed/fail at generating coherent text. |
| Outcome: | This paper will discuss how and why these models succeed/fail at generating coherent text, and provide insights on several applications. |