We present a methodology to augment task-oriented dialogue systems with logical reasoning capabilities using pretrained language models. Our experiments indicate that using our proposed methodology transformer based models can be finetuned to perform logical reasoning to: answer questions when the dialogue context contains all the required information, otherwise extract appropriate constraints to pass to downstream components (e.g. a knowledge base) when only partial information is available.

Logical Reasoning for Task Oriented Dialogue Systems

Users interacting with voice assistants today need to phrase their requests in a very specific manner to elicit an appropriate response. This limits the user experience, and is partly due to the lack of reasoning capabilities of dialogue platforms and the hand-crafted rules that require extensive labor. One possible way to improve user experience and relieve the manual efforts of designers is to build an end-to-end dialogue system that can do reasoning itself while perceiving user's utterances. In this work, we propose a novel method to incorporate the knowledge reasoning capability into dialogue systems in a more scalable and generalizable manner. Our proposed method allows a single transformer model to directly walk on a large-scale knowledge graph to generate responses. To the best of our knowledge, this is the first work to have transformer models generate responses by reasoning over differentiable knowledge graphs. We investigate the reasoning abilities of the proposed method on both task-oriented and domain-specific chit-chat dialogues. Empirical results show that this method can effectively and efficiently incorporate a knowledge graph into a dialogue system with fully-interpretable reasoning paths.

Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems

**Organizers:** :Shervin Malmasi, Eugene Agichtein, Surya Kallumadi, Nicola Ueffing, Oleg Rokhlenko, Ido
Guy
 **Description:** NLP and IR have been closely linked to e-commerce applications since the early days of the fields. This close relationship
between the two is evidenced by early publications as well as the growing number of recent publications at the intersection
of the two areas. Today, NLP and IR play a significant role in e-commerce tasks, including product search, recommender
systems, product question answering, sentiment analysis, product description and review summarization, chatbots and
shopping assistants, and customer review processing, among many other tasks being investigated by researchers in the
field. These methods play a key part in today’s online retail and shopping landscape, and continue to evolve and further
enhance the customer experience. The ECNLP workshop aims to provide a venue for the dissemination of NLP/IR
research related to e-commerce and online shopping, bringing together researchers from both academia and industry.
 
**Please visit our [website](https://sites.google.com/view/ecnlp)**

(ECNLP) The Fifth Workshop on e-Commerce and NLP

workshop paper

# Welcome everyone to ACL 2022!

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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

Alessandra Cervone

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Logical Reasoning for Task Oriented Dialogue Systems

Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems

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