Here's our daily update in tweets, live from IJCAI (International Joint Conference on Artificial Intelligence) in Macau. Like yesterday, we'll be covering tutorials and workshops. Now attending the #tutorial "Argumentation and Machine Learning: When the Whole is Greater than the Sum of its Parts" by @CeruttiFederico, & learning about #ML mechanisms that create, annotate, analyze & evaluate arguments expressed in natural language.#AI Now: "Dialogues with Socially Aware Robot Agents – Knowledge & Reasoning using Natural Language," an invited #IJCAI2019 talk by Prof. Kristiina Jokinen Her start: "The quality of #intelligence possessed by humans and #AI is fundamentally different."#Bridging2019 On his second slide: #AGI "needs fresh methods with cognitive architectures and philosophy of mind."#AI
The main IJCAI2019 conference started on August 13th. The organizers gave the opening remarks and statistics, and announced the award winners for this year. I was last here 22 years ago, and it's nice to be back. Over the next 2-3 days I'm hoping to post a few links and descriptions of the exciting search work my students and colleagues will be presenting here. Super proud that our paper got distinguished paper honorable mention today @IJCAIconf #ijcai2019 .
On the other hand, AM has rapidlyfrom a given document (Lippi 2016). Recent years have seen the development evolved by exploiting state-of-the-art neural architectures of a large number of techniques in this area, on coming from deep learning. So far, the wake of the advancements produced by deep these two worlds have progressed largely independently learning on the whole research field of natural of each other. Only recently, a few works language processing (NLP). Yet, it is widely recognized have taken some steps towards the integration of that the existing AM systems still have such methods, by applying techniques combining a large margin of improvement, as good results sub-symbolic classifiers with knowledge expressed have been obtained with some genres where prior in the form of rules and constraints to AM. knowledge on the structure of the text eases some Niculae et al. (2017) adopted structuredFor instance, AM tasks, but other genres such as legal cases support vector machines and recurrent neural and social media documents still require more networks to collectively classify argument components work (Cabrio and Villata, 2018). Performing and and their relations in short documents, understanding argumentation requires advanced by hard-coding contextual dependencies and constraints reasoning capabilities that are natural skills for humans, of the argument model in a factor graph. but which are difficult to learn for a machine. A joint inference approach for argument component Understanding whether a given piece of classification and relation identification was evidence supports a given claim, or whether two Persing and Ng (2016), followinginstead proposed by claims attack each other, are complex problems a pipeline scheme where integer linear programming that humans are able to address thanks to their is used to enforce mathematical constraints ability to exploit commonsense knowledge, and to on the outcomes of a first-stage set of classifiers.