公开(公告)号：US20240111964A1

公开(公告)日：2024-04-04

申请号：US18454136

申请日：2023-08-23

Applicant: Tata Consultancy Services Limited

Inventor： Arpita KUNDU ,  Subhasish Ghosh ,  Pratik Saini ,  Indrajit Bhattacharya ,  Tapas Nayak

IPC: G06F40/40 ,  G06F16/35 ,  G06F40/137 ,  G06F40/186

CPC classification number: G06F40/40 ,  G06F16/35 ,  G06F40/137 ,  G06F40/186

Abstract: Technical interviewing is important for organizations for assessing a candidate to make hiring decision. For effective technical interviewing, predicting difficulty of long form technical questions is crucial. The present disclosure provides systems and methods for predicting difficulty of long form technical questions using weak supervision from textbooks. Further, zero shot pre-trained large language models and unsupervised template-based technique are used for generating questions. Furthermore, a difficulty score is assigned to the generated questions based on context difficulty and task difficulty. The context difficulty for the generated questions is computed using hierarchical structure of the textbooks, and the task difficulty is computed by determining a similarity between the generated questions and Bloom's taxonomy levels. In the present disclosure, few supervised question difficulty prediction models are trained by means of weak supervision using the generated questions and corresponding difficulty scores and further evaluated for prediction performance using a gold-standard question difficulty dataset.

公开(公告)号：US12111856B2

公开(公告)日：2024-10-08

申请号：US18470657

申请日：2023-09-20

Applicant: Tata Consultancy Services Limited

Inventor： Anumita Dasguptabandyopadhyay ,  Prabir Mallick ,  Tapas Nayak ,  Indrajit Bhattacharya ,  Sangameshwar Suryakant Patil

IPC: G06F16/30 ,  G06F16/31 ,  G06F16/332

CPC classification number: G06F16/31 ,  G06F16/3329

Abstract: This disclosure relates generally to long-form answer extraction and, more particularly, to long-form answer extraction based on combination of sentence index generation techniques. Existing answer extractions techniques have achieved significant progress for extractive short answers; however, less progress has been made for long form questions that require explanations. Further the state-of-art long-answer extractions techniques result in poorer long-form answers or not address sparsity which becomes an issue longer contexts. Additionally, pre-trained generative sequence-to-sequence models are gaining popularity for factoid answer extraction tasks. Hence the disclosure proposes a long-form answer extraction based on several steps including training a set of generative sequence-to-sequence models comprising a sentence indices generation model and a sentence index spans generation. The trained set of generative sequence-to-sequence models is further utilized for model long-form answer extraction based on a union of several sentence index generation techniques comprising a sentence indices and a sentence index spans.

公开(公告)号：US11880345B2

公开(公告)日：2024-01-23

申请号：US17463591

申请日：2021-09-01

Applicant: Tata Consultancy Services Limited

Inventor： Atreya Bandyopadhyay ,  Indrajit Bhattacharya ,  Rajdip Chowdhury ,  Debayan Mukherjee

IPC: G06F16/21

CPC classification number: G06F16/211

Abstract: This disclosure relates generally to generating annotations and field-names for a relational schema. Typically, most domains have relational database (RDB) system built for them instead of domain ontologies and usually linguistic information of the schema is not used to recover the domain terms. The disclosed method and system facilitate generating annotations and field-names for a relational schema, while considering the linguistic information of a schema by using a trained model, trained through a proposed training technique. The trained model comprises of at least one knowledge graph and a set of associated parameters. The trained model is further used to perform a plurality of tasks, wherein the plurality of tasks include generating a plurality of new fieldnames for a relational schema through a stochastic generative process and for generating a new annotation for a fieldname of a relational schema through a probabilistic inference technique.

公开(公告)号：US12135736B2

公开(公告)日：2024-11-05

申请号：US17822714

申请日：2022-08-26

Applicant: Tata Consultancy Services Limited

Inventor： Sangameshwar Suryakant Patil ,  Samiran Pal ,  Avinash Kumar Singh ,  Soham Datta ,  Girish Keshav Palshikar ,  Indrajit Bhattacharya ,  Harsimran Bedi ,  Yash Agrawal ,  Vasudeva Varma Kalidindi

IPC: G06F40/00 ,  G06F16/332 ,  G06F16/35 ,  G06F40/295 ,  G06F40/30 ,  G10L15/18 ,  G10L15/183 ,  G10L15/22

Abstract: Questions play a central role in assessment of a candidate's expertise during an interview or examination. However, generating such questions from input text documents manually needs specialized expertise and experience. Further, techniques that are available for automated question generation require input sentence as well as an answer phrase in that sentence to generate question. This in-turn requires large training datasets consisting tuples of input sentence answer-phrase and the corresponding question. Additionally, training datasets are available are for general purpose text, but not for technical text. Present application provides systems and methods for generating technical questions from technical documents. The system extracts meta information and linguistic information of text data present in technical documents. The system then identifies relationships that exist in provided text data. The system further creates one or more graphs based on the identified relationships. The created graphs are the used by the system to generate technical questions.

公开(公告)号：US11328726B2

公开(公告)日：2022-05-10

申请号：US17009317

申请日：2020-09-01

Applicant: Tata Consultancy Services Limited

Inventor： Pradip Pramanick ,  Chayan Sarkar ,  Balamuralidhar Purushothaman ,  Ajay Kattepur ,  Indrajit Bhattacharya ,  Arpan Pal

IPC: G06F17/00 ,  G10L15/22 ,  G06F40/205 ,  G06F40/30

Abstract: This disclosure relates generally to human-robot interaction (HRI) to enable a robot to execute tasks that are conveyed in a natural language. The state-of-the-art is unable to capture human intent, implicit assumptions and ambiguities present in the natural language to enable effective robotic task identification. The present disclosure provides accurate task identification using classifiers trained to understand linguistic and semantic variations. A mixed-initiative dialogue is employed to resolve ambiguities and address the dynamic nature of a typical conversation. In accordance with the present disclosure, the dialogues are minimal and directed to the goal to ensure human experience is not degraded. The method of the present disclosure is also implemented in a context sensitive manner to make the task identification effective.