# Answer Generation for Questions With Multiple Information Sources in E-Commerce

arXiv: https://arxiv.org/abs/2111.14003 · HTML: https://mlsanity.com/paper/2111.14003/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Addressing Complex and Subjective Product-Related Queries with Customer Reviews](https://mlsanity.com/paper/1512.06863/) (2015)
- [Product-Aware Answer Generation in E-Commerce Question-Answering](https://mlsanity.com/paper/1901.07696/) (2019)
- [Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce](https://mlsanity.com/paper/1711.08726/) (2017)
- [Meaningful Answer Generation of E-Commerce Question-Answering](https://mlsanity.com/paper/2011.07307/) (2020)
- [Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce](https://mlsanity.com/paper/2008.11972/) (2020)
- [Review-Driven Answer Generation for Product-Related Questions in E-Commerce](https://mlsanity.com/paper/1905.01994/) (2019)
- [A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification](https://mlsanity.com/paper/1805.01089/) (2018)
- [Distantly Supervised Transformers For E-Commerce Product QA](https://mlsanity.com/paper/2104.02947/) (2021)

## Abstract (arXiv)

Automatic question answering is an important yet challenging task in E-commerce given the millions of questions posted by users about the product that they are interested in purchasing. Hence, there is a great demand for automatic answer generation systems that provide quick responses using related information about the product. There are three sources of knowledge available for answering a user posted query, they are reviews, duplicate or similar questions, and specifications. Effectively utilizing these information sources will greatly aid us in answering complex questions. However, there are two main challenges present in exploiting these sources: (i) The presence of irrelevant information and (ii) the presence of ambiguity of sentiment present in reviews and similar questions. Through this work we propose a novel pipeline (MSQAP) that utilizes the rich information present in the aforementioned sources by separately performing relevancy and ambiguity prediction before generating a response. Experimental results show that our relevancy prediction model (BERT-QA) outperforms all other variants and has an improvement of 12.36% in F1 score compared to the BERT-base baseline. Our generation model (T5-QA) outperforms the baselines in all content preservation metrics such as BLEU, ROUGE and has an average improvement of 35.02% in ROUGE and 198.75% in BLEU compared to the highest performing baseline (HSSC-q). Human evaluation of our pipeline shows us that our method has an overall improvement in accuracy of 30.7% over the generation model (T5-QA), resulting in our full pipeline-based approach (MSQAP) providing more accurate answers. To the best of our knowledge, this is the first work in the e-commerce domain that automatically generates natural language answers combining the information present in diverse sources such as specifications, similar questions, and reviews data.