Papers with online shopping
Domain-specific transformer models for query translation (2023.acl-industry)
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| Challenge: | In domains such as Grocery, users prefer to buy certain brands of products . a large non-English speaking population makes it difficult to translate code-mix queries . |
| Approach: | They propose a model to preserve/correct Grocery brand names while translating context words . they propose to use a dataset of popular Groceries brand names to train the model . |
| Outcome: | The proposed model preserves/corrects Grocery brand names while translating context words . it is tested with a large non-English speaking population and is deployed in production . |
Soft Self-Consistency Improves Language Models Agents (2024.acl-short)
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| Challenge: | Current “sample and select” methods rely on majority voting to score answers . however, when tasks have many distinct and valid answers, selection by voting requires a large number of samples. |
| Approach: | They introduce a method that replaces SC's discontinuous scoring with a continuous score computed from model likelihoods to increase selection even when actions are sparsely distributed. |
| Outcome: | The proposed method improves performance and efficiency on long-horizon interactive tasks by replacing SC’s discontinuous scoring with a continuous score computed from model likelihoods. |
Identifying Helpful Sentences in Product Reviews (2021.naacl-main)
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| Challenge: | a key advantage of online shopping is the ability to read what other customers are saying about products of interest. |
| Approach: | They propose a task to extract a representative helpful sentence from reviews . they collect a dataset in english and use crowd-sourcing to test their model . |
| Outcome: | The proposed model outperforms baselines in a crowd-sourced model of representative helpful sentences from product reviews. |
Persona or Context? Towards Building Context adaptive Personalized Persuasive Virtual Sales Assistant (2022.aacl-main)
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Abhisek Tiwari, Sriparna Saha, Shubhashis Sengupta, Anutosh Maitra, Roshni Ramnani, Pushpak Bhattacharyya
| Challenge: | Existing task-oriented conversational agents assume that end-users will always have a pre-determined and servable task goal, which results in dialogue failure in hostile scenarios, such as goal unavailability. |
| Approach: | They propose to build an end-to-end multi-modal persuasive dialogue system incorporating a personalized persuasive module aided goal controller and goal persuader. |
| Outcome: | The proposed system achieves user tasks even in goal unavailability scenarios by persuading them towards a similar and servable goal. |
AnswerFact: Fact Checking in Product Question Answering (2020.emnlp-main)
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| Challenge: | a product-related community question answering platform is widely employed in many E-commerce sites . however, the misinformation in the answers on those platforms poses unprecedented challenges for users to obtain reliable and truthful product information. |
| Approach: | They propose a large scale fact checking dataset from product question answering forums to predict the answer veracity . each answer is accompanied by its veraity label and associated evidence sentences . |
| Outcome: | The proposed model outperforms baselines on the question veracity prediction task. |
VoiSeR: A New Benchmark for Voice-Based Search Refinement (2021.eacl-main)
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| Challenge: | a new study shows that voice-based search systems are challenging to support in the context of the user intent of voice searches . support for voice-driven search, exploration, and refinement is a fundamental aspect of voice assistants . |
| Approach: | They propose to use crowdsourcing to collect voice-based search refinements . they use 10,000 search refinement utterances to annotate a search intent . |
| Outcome: | The proposed dataset shows that voice-based search refinements can support most common tasks . the study shows that the proposed dataset can support research in conversational query understanding . |
Evaluating Cultural and Social Awareness of LLM Web Agents (2025.findings-naacl)
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Haoyi Qiu, Alexander Fabbri, Divyansh Agarwal, Kung-Hsiang Huang, Sarah Tan, Nanyun Peng, Chien-Sheng Wu
| Challenge: | Existing benchmarks often overlook cultural and social awareness . current evaluations focus on task completion, often ignoring the diverse cultural and socio-cultural backgrounds. |
| Approach: | They propose a benchmark to assess LLM agents’ sensitivity to cultural and social norms across two web-based tasks: online shopping and social discussion forums. |
| Outcome: | The proposed framework evaluates LLM agents’ ability to detect and appropriately respond to norm-violating user queries and observations across two web-based tasks. |
End-to-End Conversational Search for Online Shopping with Utterance Transfer (2021.emnlp-main)
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Liqiang Xiao, Jun Ma, Xin Luna Dong, Pascual Martínez-Gómez, Nasser Zalmout, Chenwei Zhang, Tong Zhao, Hao He, Yaohui Jin
| Challenge: | a new study proposes a conversational search system that integrates product attributes and dialog with search . but it faces two real world challenges: imperfect product schema/knowledge and lack of training dialog data . |
| Approach: | They propose an end-to-end conversational search system that integrates search with text . they propose an utterance transfer approach that generates dialogue utterations from other domains . |
| Outcome: | The proposed system outperforms the best tested baseline in a conversational search dataset for online shopping. |