Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.09398 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915240221868032 |
|---|---|
| author | Kumar, Gaurav Dandu, Murali Mohana Krishna |
| author_facet | Kumar, Gaurav Dandu, Murali Mohana Krishna |
| contents | There has been a lot of progress towards building NLP models that scale to multiple tasks. However, real-world systems contain multiple components and it is tedious to handle cross-task interaction with varying levels of text granularity. In this work, we built an end-to-end Ranking and Question-Answering (QA) system using Forte, a toolkit that makes composable NLP pipelines. We utilized state-of-the-art deep learning models such as BERT, RoBERTa in our pipeline, evaluated the performance on MS-MARCO and Covid-19 datasets using metrics such as BLUE, MRR, F1 and compared the results of ranking and QA systems with their corresponding benchmark results. The modular nature of our pipeline and low latency of reranker makes it easy to build complex NLP applications easily. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09398 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Composable NLP Workflows for BERT-based Ranking and QA System Kumar, Gaurav Dandu, Murali Mohana Krishna Computation and Language Artificial Intelligence There has been a lot of progress towards building NLP models that scale to multiple tasks. However, real-world systems contain multiple components and it is tedious to handle cross-task interaction with varying levels of text granularity. In this work, we built an end-to-end Ranking and Question-Answering (QA) system using Forte, a toolkit that makes composable NLP pipelines. We utilized state-of-the-art deep learning models such as BERT, RoBERTa in our pipeline, evaluated the performance on MS-MARCO and Covid-19 datasets using metrics such as BLUE, MRR, F1 and compared the results of ranking and QA systems with their corresponding benchmark results. The modular nature of our pipeline and low latency of reranker makes it easy to build complex NLP applications easily. |
| title | Composable NLP Workflows for BERT-based Ranking and QA System |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2504.09398 |