Digital assistants have become ubiquitous in e-commerce applications, following the recent advancements in Information Retrieval (IR), Natural Language Processing (NLP) and Generative Artificial Intelligence (AI). However, customers are often unsure or unaware of how to effectively converse with these assistants to meet their shopping needs. In this work, we emphasize the importance of providing customers a fast, easy to use, and natural way to interact with conversational shopping assistants. We propose a framework that employs Large Language Models (LLMs) to automatically generate contextual, useful, answerable, fluent and diverse questions about products, via in-context learning and supervised fine-tuning. Recommending these questions to customers as helpful suggestions or hints to both start and continue a conversation can result in a smoother and faster shopping experience with reduced conversation overhead and friction. We perform extensive offline evaluations, and discuss in detail about potential customer impact, and the type, length and latency of our generated product questions if incorporated into a real-world shopping assistant.

Natural Language Processing (NLP) technology plays a key role in e-commerce today, where this technology can be used for a range of tasks, such as improving search results, providing recommendations, and powering virtual assistants. The ECNLP workshop series focuses on NLP and Machine Learning methods for e-commerce, with a focus on applied and fundamental machine learning and NLP methods that can be leveraged in applied settings. The workshop aims to being together researchers from both industry and academia, with the goal of fostering greater knowledge sharing and collaboration between researchers and practitioners in this field.

Conversation disentanglement aims to identify and group utterances from a conversation into separate threads. Existing methods primarily focus on disentangling multi-party conversations with three or more speakers, explicitly or implicitly incorporating speaker-related feature signals to disentangle. Most existing models require a large amount of human annotated data for model training, and often focus on pairwise relations between utterances, not accounting much for the conversational context. In this work, we propose a multi-task learning approach with a contrastive learning objective, DiSC, to disentangle conversations between two speakers -- a user and a virtual speech assistant, for a novel domain of e-commerce. We analyze multiple ways and granularities to define conversation "threads''. DiSC jointly learns the relation between pairs of utterances, as well as between utterances and their respective thread context. We train and evaluate our models on multiple multi-threaded conversation datasets that were automatically created, without any human labeling effort. Experimental results on public datasets as well as real-world shopping conversations from a commercial speech assistant show that DiSC outperforms state-of-the-art baselines by at least 3%, across both automatic and human evaluation metrics. We also demonstrate how DiSC improves downstream dialog response generation in the shopping domain.

An essential part of making shopping purchase decisions is to compare and contrast products based on key differentiating features, but doing this manually can be overwhelming. Prior methods offer limited product comparison capabilities, e.g., via pre-defined common attributes that may be difficult to understand, or irrelevant to a particular product or user. Automatically generating an informative, natural-sounding, and factually consistent comparative text for multiple product and attribute types is a challenging research problem. We describe HCPC (Human Centered Product Comparison), to tackle two kinds of comparisons for online shopping: (i) product-specific, to describe and compare products based on their key attributes; and (ii) attribute-specific comparisons, to compare similar products on a specific attribute. To ensure that comparison text is faithful to the input product data, we introduce a novel multi-decoder, multi-task generative language model. One decoder generates product comparison text, and a second one generates supportive, explanatory text in the form of product attribute names and values. The second task imitates a copy mechanism, improving the comparison generator, and its output is used to justify the factual accuracy of the generated comparison text, by training a factual consistency model to detect and correct errors in the generated comparative text. We release a new dataset (https://registry.opendata.aws/) of ~15K human generated sentences, comparing products on one or more attributes (the first such data we know of for product comparison). We demonstrate on this data that HCPC significantly outperforms strong baselines, by ~10% using automatic metrics, and ~5% using human evaluation.

Providing a justification or explanation for a recommendation has been shown to improve the users’ experience with recommender systems, in particular by increasing confidence in the recommendations. However, in order to be effective in a conversational setting, the justifications have to be appropriate for the conversation so far. Previous approaches rely on a user history of reviews and ratings of related items to personalize the recommendation, but this information is not generally available when conversing with a new user, and as such a cold-start problem imposes a challenge in generating suitable justifications. To address this problem, we propose and validate a new method, CONJURE (CONversational JUstificatons for REcommendations) to generate contextually relevant justifications for conversational recommendations. Specifically, we investigate whether the conversation itself can be used effectively to model the user, identify relevant review content from other users, and generate a justification that boosts the user’s confidence in and understanding of the recommendation. To implement CONJURE, we test several novel extensions to prior algorithms, by exploiting an auxiliary corpus of movie reviews to construct the justifications from extracted pieces of those reviews. In particular, we explore different conversation representations and ranking approaches. To evaluate CONJURE, we developed a pairwise crowd task to compare justifications. Our results show large, significant improvements in Efficiency and Transparency metrics over the previous non-contextualized template-based methods. We plan to release our code and an augmented conversation corpus on Github.

Multi-Task Learning (MTL) is widely-accepted in Natural Language Processing as a standard technique for learning multiple related tasks in one model. Training an MTL model requires having the training data for all tasks available at the same time. As systems usually evolve over time, (e.g., to support new functionalities), adding a new task to an existing MTL model usually requires retraining the model from scratch on all the tasks and this can be time-consuming and computationally expensive. Moreover, in some scenarios, the data used to train the original training may be no longer available, for example, due to storage or privacy concerns.

Named Entity Recognition (NER) is a crucial natural language understanding task for many down-stream tasks such as question answering and retrieval. Despite significant progress in developing NER models for multiple languages and domains, scaling to emerging and/or low-resource domains still remains challenging, due to the costly nature of acquiring training data. We propose CycleNER, an unsupervised approach based on cycle-consistency training that uses two functions: (i) sentence-to-entity – S2E and (ii) entity-to-sentence – E2S, to carry out the NER task. CycleNER does not require annotations but a set of sentences with no entity labels and another independent set of entity examples. Through cycle-consistency training, the output from one function is used as input for the other (e.g. S2E → E2S) to align the representation spaces of both functions and therefore enable unsupervised training. Evaluation on several domains comparing CycleNER against supervised and unsupervised competitors shows that CycleNER achieves highly competitive performance with only a few thousand input sentences. We demonstrate competitive performance against supervised models, achieving 73% of supervised performance without any annotations on CoNLL03, while significantly outperforming unsupervised approaches.

Named entity recognition (NER) for Web queries is very challenging. Queries often do not consist of well-formed sentences, and contain very little context, with highly ambiguous queried entities. Code-mixed queries, with entities in a different language than the rest of the query, pose a particular challenge in domains like e-commerce (e.g. queries containing movie or product names). This work tackles NER for code-mixed queries, where entities and non-entity query terms co-exist simultaneously in different languages. Our contributions are twofold. First, to address the lack of code-mixed NER data we create EMBER, a large-scale dataset in six languages with four different scripts. Based on Bing query data, we include numerous language combinations that showcase real-world search scenarios. Secondly, we propose a novel gated architecture that enhances existing multi-lingual Transformers with a Mixture-of-Experts model to dynamically infuse multi-lingual gazetteers, allowing it to simultaneously differentiate and handle entities and non-entity query terms in multiple languages. Experimental evaluation on code-mixed queries in several languages shows that our approach efficiently utilizes gazetteers to recognize entities in code-mixed queries with an F1=68%, an absolute improvement of +31% over a non-gazetteer baseline.

Even though Automatic Speech Recognition (ASR) systems significantly improved over the last decade, they still introduce a lot of errors when they transcribe voice to text. One of the most common reasons for these errors is phonetic confusion between similar-sounding expressions. As a result, ASR transcriptions often contain "quasi-oronyms", i.e., words or phrases that sound similar to the source ones, but that have completely different semantics (e.g., "win" instead of "when" or "accessible on defecting" instead of "accessible and affecting"). These errors significantly affect the performance of downstream Natural Language Understanding (NLU) models (e.g., intent classification, slot filling, etc.) and impair user experience. To make NLU models more robust to such errors, we propose novel phonetic-aware text representations. Specifically, we represent ASR transcriptions at the phoneme level, aiming to capture pronunciation similarities, which are typically neglected in word-level representations (e.g., word embeddings). To train and evaluate our phoneme representations, we generate noisy ASR transcriptions of four existing datasets - Stanford Sentiment Treebank, SQuAD, TREC Question Classification and Subjectivity Analysis - and show that common neural network architectures exploiting the proposed phoneme representations can effectively handle noisy transcriptions and significantly outperform state-of-the-art baselines. Finally, we confirm these results by testing our models on real utterances spoken to the Alexa virtual assistant.

Questions and their corresponding answers within a community based question answering (CQA) site are frequently presented as top search results forWeb search queries and viewed by millions of searchers daily. The number of answers for CQA questions ranges from a handful to dozens, and a searcher would be typically interested in the different suggestions presented in various answers for a question. Yet, especially when many answers are provided, the viewer may not want to sift through all answers but to read only the top ones. Prior work on answer ranking in CQA considered the qualitative notion of each answer separately, mainly whether it should be marked as best answer. We propose to promote CQA answers not only by their relevance to the question but also by the diversification and novelty qualities they hold compared to other answers. Specifically, we aim at ranking answers by the amount of new aspects they introduce with respect to higher ranked answers (novelty), on top of their relevance estimation. This approach is common in Web search and information retrieval, yet it was not addressed within the CQA settings before, which is quite different from classic document retrieval. We propose a novel answer ranking algorithm that borrows ideas from aspect ranking and multi-document summarization, but adapts them to our scenario. Answers are ranked in a greedy manner, taking into account their relevance to the question as well as their novelty compared to higher ranked answers and their coverage of important aspects. An experiment over a collection of Health questions, using a manually annotated gold-standard dataset, shows that considering novelty for answer ranking improves the quality of the ranked answer list.

It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history. The lack of historical ratings results in the user and the item cold-start problems. The latter is the main focus of this work. Most of the current literature addresses this problem by integrating content-based recommendation techniques to model the new item. However, in many cases such content is not available, and the question arises is whether this problem can be mitigated using CF techniques only. We formalize this problem as an optimization problem: given a new item, a pool of available users, and a budget constraint, select which users to assign with the task of rating the new item in order to minimize the prediction error of our model. We show that the objective function is monotone-supermodular, and propose efficient optimal design based algorithms that attain an approximation to its optimum. Our findings are verified by an empirical study using the Netflix dataset, where the proposed algorithms outperform several baselines for the problem at hand.