Search Results for author:
Found 16 papers, 4 papers with code
Pre-training Synthetic Cross-lingual Decoder for Multilingual Samples Adaptation in E-Commerce Neural Machine Translation
We perform the experiments over eight low-resource and three high resource language pairs from the generic domain, and two language pairs from the product review domains.
Low Resource Chat Translation: A Benchmark for Hindi–English Language Pair
Chatbots are used in various sectors such as banking, healthcare, e-commerce, etc, and are mainly available in English.
Product Review Translation using Phrase Replacement and Attention Guided Noise Augmentation
Given that 44% of Indian population speaks and operates in Hindi language and we address the above challenges by presenting an English–to–Hindi neural machine translation (NMT) system to translate the product reviews available on e-commerce websites by creating an in-domain parallel corpora and handling various types of noise in reviews via two data augmentation techniques and viz.
Product Review Translation: Parallel Corpus Creation and Robustness towards User-generated Noisy Text
We train an English–to–Hindi neural machine translation (NMT) system to translate the product reviews available on e-commerce websites.
Sentiment Preservation in Review Translation using Curriculum-based Re-inforcement Framework
Machine Translation (MT) systems often fail to preserve different stylistic and pragmatic properties of the source text (e. g. sentiment and emotion and gender traits and etc.)
Product Description and QA Assisted Self-Supervised Opinion Summarization
For evaluation, due to the unavailability of test sets with additional sources, we extend the Amazon, Oposum+, and Flipkart test sets and leverage ChatGPT to annotate summaries.
Leveraging Domain Knowledge for Efficient Reward Modelling in RLHF: A Case-Study in E-Commerce Opinion Summarization
While this strategy has proven effective, the training methodology requires a lot of human preference annotation (usually in the order of tens of thousands) to train $\varphi$.
One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation
Evaluation of opinion summaries using conventional reference-based metrics rarely provides a holistic evaluation and has been shown to have a relatively low correlation with human judgments.
Rapid Speaker Adaptation in Low Resource Text to Speech Systems using Synthetic Data and Transfer learning
Using transfer learning from high-resource language and synthetic corpus we present a low-cost solution to train a custom TTS model.
Code-Mixed Text to Speech Synthesis under Low-Resource Constraints
We further present an exhaustive evaluation of single-speaker adaptation and multi-speaker training with Tacotron2 + Waveglow setup to show that the former approach works better.
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards
Translating questions using Neural Machine Translation (NMT) poses more challenges, especially in noisy environments, where the grammatical correctness of the questions is not monitored.
Building Accurate Low Latency ASR for Streaming Voice Search
Our model achieves a word error rate (WER) of 3. 69% without EOS and 4. 78% with EOS while also reducing the search latency by approximately ~1300 ms (equivalent to 46. 64% reduction) when compared to an independent voice activity detection (VAD) model.
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English
Automation of on-call customer support relies heavily on accurate and efficient speech-to-intent (S2I) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Study of Encoder-Decoder Architectures for Code-Mix Search Query Translation
We propose a transformer-based approach for code-mix query translation to enable users to search with these queries.
Vernacular Search Query Translation with Unsupervised Domain Adaptation
For demonstration, we show results for Hindi to English query translation and use mBART-large-50 model as the baseline to improve upon.
Answer Generation for Questions With Multiple Information Sources in E-Commerce
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.
