公开(公告)号：US20230109692A1

公开(公告)日：2023-04-13

申请号：US17822722

申请日：2022-08-26

Applicant: Tata Consultancy Services Limited

Inventor： ANUMITA DASGUPTA ,  INDRAJIT BHATTACHARYA ,  GIRISH KESHAV PALSHIKAR ,  PRATIK SAINI ,  SANGAMESHWAR SURYAKANT PATIL ,  SOHAM DATTA ,  PRABIR MALLICK ,  SAMIRAN PAL ,  SUNIL KUMAR KOPPARAPU ,  AISHWARYA CHHABRA ,  AVINASH KUMAR SINGH ,  KAUSTUV MUKHERJI ,  MEGHNA ABHISHEK PANDHARIPANDE ,  ANIKET PRAMANICK ,  ARPITA KUNDU ,  SUBHASISH GHOSH ,  CHANDRASEKHAR ANANTARAM ,  ANAND SIVASUBRAMANIAM ,  GAUTAM SHROFF

IPC: G06Q10/10 ,  G06Q10/06 ,  H04L12/18

Abstract: This disclosure relates generally to method and system for providing assistance to interviewers. Technical interviewing is immensely important for enterprise but requires significant domain expertise and investment of time. The present disclosure aids assists interviewers with a framework via an interview assistant bot. The method initiates an interview session for a job description by selecting a set of qualified candidates resume to be interviewed. Further, the IA bot recommends each interviewer with a set of question and reference answer pairs prior initiating the interview. At each interview step, the IA bot records interview history and recommends interviewer with the revised set of questions. Further, an assessment score is determined for the candidate using the reference answer extracted from a resource corpus. Additionally, statistics about the interview process is generated, such as number and nature of questions asked, and its variation across to identify outliers for corrective actions.

公开(公告)号：US20240126791A1

公开(公告)日：2024-04-18

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: G06F16/31 ,  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：US20240119075A1

公开(公告)日：2024-04-11

申请号：US18479646

申请日：2023-10-02

Applicant: Tata Consultancy Services Limited

Inventor： PRABIR MALLICK ,  SAMIRAN PAL ,  AVINASH KUMAR SINGH ,  ANUMITA DASGUPTA ,  SOHAM DATTA ,  KAAMRAAN KHAN ,  TAPAS NAYAK ,  INDRAJIT BHATTACHARYA ,  GIRISH KESHAV PALSHIKAR

IPC: G06F16/332 ,  G06F16/33 ,  G06F40/186 ,  G06F40/284 ,  G06F40/289 ,  G06F40/30 ,  G06F40/40

CPC classification number: G06F16/3329 ,  G06F16/3344 ,  G06F40/186 ,  G06F40/284 ,  G06F40/289 ,  G06F40/30 ,  G06F40/40

Abstract: Conventional Question and Answer (QA) datasets are created for generating factoid questions only and the present disclosure generates longform technical QA dataset from textbooks. Initially, the system receives a technical textbook document and extracts a plurality of contexts. Further, a first plurality of questions are generated based on the plurality of contexts. A plurality of answerable questions are generated further based on the plurality of contexts using an unsupervised template-based matching technique. Further, a combined plurality of questions are generated by combining the first plurality of questions and the plurality of answerable questions. Further, an answer for the combined plurality of questions are generated using an autoregressive language model and a mapping score is computed. Further, a plurality of optimal answers are selected based on the corresponding mapping score. Finally, a longform technical question and answer dataset is generated based on the combined plurality of questions and optimal answers.