Multi-level Memory for Task Oriented Dialogs

Recent end-to-end task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. Current work makes simplifying assumptions about the structure of the knowledge base, such as the use of triples to represent knowledge, and combines dialog utterances (context) as well as knowledge base (KB) results as part of the same memory. This causes an explosion in the memory size, and makes the reasoning over memory harder. In addition, such a memory design forces hierarchical properties of the data to be fit into a triple structure of memory. This requires the memory reader to infer relationships across otherwise connected attributes. In this paper we relax the strong assumptions made by existing architectures and separate memories used for modeling dialog context and KB results. Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results. The multi-level memory first addresses queries, followed by results and finally each key-value pair within a result. We conduct detailed experiments on three publicly available task oriented dialog data sets and we find that our method conclusively outperforms current state-of-the-art models. We report a 15-25% increase in both entity F1 and BLEU scores.Comment: Accepted as full paper at NAACL 201

Edge Replacement Grammars: A Formal Language Approach for Generating Graphs

Graphs are increasingly becoming ubiquitous as models for structured data. A generative model that closely mimics the structural properties of a given set of graphs has utility in a variety of domains. Much of the existing work require that a large number of parameters, in fact exponential in size of the graphs, be estimated from the data. We take a slightly different approach to this problem, leveraging the extensive prior work in the formal graph grammar literature. In this paper, we propose a graph generation model based on Probabilistic Edge Replacement Grammars (PERGs). We propose a variant of PERG called Restricted PERG (RPERG), which is analogous to PCFGs in string grammar literature. With this restriction, we are able to derive a learning algorithm for estimating the parameters of the grammar from graph data. We empirically demonstrate on real life datasets that RPERGs outperform existing methods for graph generation. We improve on the performance of the state-of-the-art Hyperedge Replacement Grammar based graph generative model. Despite being a context free grammar, the proposed model is able to capture many of the structural properties of real networks, such as degree distributions, power law and spectral characteristics.Comment: To be presented at SIAM International Conference on Data Mining (SDM19). arXiv admin note: text overlap with arXiv:1802.08068, arXiv:1608.03192 by other author

Reduced basis method for Boltzmann equation

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2006.Includes bibliographical references (p. 103-106).The main aim of the project is to solve the BGK model of the Knudsen parameterized Boltzmann equation which is 1-d with respect to both space and velocity. In order to solve the Boltzmann equation, we first transform the original differential equation by replacing the dependent variable with another variable, weighted with function t(y); next we obtain a Petrov Galerkin weak form of this new transformed equation. To obtain a stable and accurate solution of this weak form, we perform a transformation of the velocity variable y, such that the semi-infinite domain is mapped into a finite domain; we choose the weighting function t(y), to balance contributions at infinity. Once we obtain an accurate and well defined finite element solution of the problem. The next step is to perform the reduced basis analysis of the equation using these accurate finite element solutions. We conclude the project by verifying that the orthonormal reduced Basis method based on the greedy algorithm converges rapidly over the chosen test space.by Revanth Reddy Garlapati.S.M

Online Payment Module

The aim of this project is to deploy the online payment service in Moodle. All the major debit, credit and international card (transactions) can be accepted for payment. Online payment module prepares a web server that takes all types of transactions. This module can be enabled by the site administrator. If it is enabled, students can pay for their classes through online transactions. Administrator can set an individual price for a course if needed. It allows the user to create their own account and add optional account links. This project is important to resolve the issues for students and administrators to have an easy glance at the course registration like selection of their courses, Fee details. This project makes it easy for students to look for the courses and register, one can check the site as a guest and can create his/her own account and can enroll for subjects. One can see the fee details for each course

Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations

Open-domain dialog involves generating search queries that help obtain relevant knowledge for holding informative conversations. However, it can be challenging to determine what information to retrieve when the user is passive and does not express a clear need or request. To tackle this issue, we present a novel approach that focuses on generating internet search queries that are guided by social commonsense. Specifically, we leverage a commonsense dialog system to establish connections related to the conversation topic, which subsequently guides our query generation. Our proposed framework addresses passive user interactions by integrating topic tracking, commonsense response generation and instruction-driven query generation. Through extensive evaluations, we show that our approach overcomes limitations of existing query generation techniques that rely solely on explicit dialog information, and produces search queries that are more relevant, specific, and compelling, ultimately resulting in more engaging responses.Comment: Accepted in EMNLP 2023 Finding

NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge

Claim detection and verification are crucial for news understanding and have emerged as promising technologies for mitigating news misinformation. However, most existing work has focused on claim sentence analysis while overlooking crucial background attributes (e.g., claimer, claim objects). In this work, we present NewsClaims, a new benchmark for knowledge-aware claim detection in the news domain. We redefine the claim detection problem to include extraction of additional background attributes related to each claim and release 889 claims annotated over 143 news articles. NewsClaims aims to benchmark claim detection systems in emerging scenarios, comprising unseen topics with little or no training data. To this end, we provide a comprehensive evaluation of zero-shot and prompt-based baselines for NewsClaims.Comment: Preprin