Product Titles-to-Attributes As a Text-to-Text Task (2022)

Abstract: Online marketplaces use attribute-value pairs, such as brand, size, size type, color, etc. to help define important and relevant facts about a listing. These help buyers to curate their search results using attribute filtering and overall create a richer experience. Although their critical importance for listings’ discoverability, getting sellers to input tens of different attribute-value pairs per listing is costly and often results in missing information. This can later translate to the unnecessary removal of relevant listings from the search results when buyers are filtering by attribute values. In this paper we demonstrate using a Text-to-Text hierarchical multi-label ranking model framework to predict the most relevant attributes per listing, along with their expected values, using historic user behavioral data. This solution helps sellers by allowing them to focus on verifying information on attributes that are likely to be used by buyers, and thus, increase the expected recall for their listings. Specifically for eBay’s case we show that using this model can improve the relevancy of the attribute extraction process by 33.2% compared to the current highly-optimized production system. Apart from the empirical contribution, the highly generalized nature of the framework presented in this paper makes it relevant for many high-volume search-driven websites.

## introduction many online marketplaces have new-listing forms that include both structured and unstructured input types to help sellers describe their listing foot_0 . while the unstructured part often includes free-text input boxes for title and description, a pictures upload option, etc., the structured part can include the selection of the listing category from a predefined list, or selecting specific attribute-value pairs (e.g. {"brand":"apple", "color":"black"}). of the two, structured input often enables marketplaces a more streamline use of the data, since it requires less preprocessing and allows for more direct usage (via search results filters, etc.). on the flip-side, entering such data is more labor intensive for the sellers, and therefore, more expensive to get. this can also be intricate work for sellers since in most cases there are tens of different possible attribute names for every listing, with some attributes having more than one possible value. to reduce the seller-inflicted cost of entering listing attribute values we set two solution guidelines: (a) sellers should focus on the top attributes that are expected to impact their listing discoverability. this aims to reduce the number of attributes for which seller attention is required and only focus on those which are likely to be used in the buyerjourney of their target audience. @xcite , in an effort to further reduce friction, the marketplace should pre-populate a suggested value for each of these top attributes. to identify the top attributes in a scalable manner we leveraged the rich historical data of buyer behavior on the ebay website. like many other search-driven websites, ebay allows buyers to curate search results by applying filters on top of the initial results from the free-text-based query. logging the filtering selections of buyers, alongside with their post-search actions, allows for an opportunity to learn what are the key attributes that buyers value when searching for the right result. for example, a common buyer behavior is to type a general description in the search box, like "handbag", and then to filter the results using more granular attributes, like "material", etc. (figure 1 ). following this filtering step, the buyer might click on, and potentially purchase, a specific listing that was a part of the filtered results set. mapping this buyer journey, from search to filtering and listing-click, allows to learn which attributes are most important for the discovery of every listing. from a modeling standpoint, to accommodate both of the solution guidelines above, the output set of the model should include the importance ranking of the top attributes and their expected value. as to the model input, in order for the solution to generalize across different downstream tasks, we need to pick a minimal viable data point that all listings have, but yet, that is highly informative. in our case that would be the listing title. model design can be examined using different lenses; a supervised model based on the historical mapping between listing titles and multiple attribute-value pairs can be modeled as a multi-label text classification (mltc) task. however, since there is a hierarchical relationship between the attributes and values (since each attribute has a finite list of possible values), the task can also be viewed as a hierarchical multi-label text classification (hmltc) task. last, since we care about the importance ranking of the attributevalue pairs, this can also be viewed as a ranking task. recent text-to-text-driven approaches have shown to be highly valuable for various natural language processing (nlp) tasks including mltc and hmltc @xcite @xcite @xcite . inspired by these approaches, we demonstrate using a text-to-text framework in a hmltc ranking task and compare it to other classification models. specifically in our case, the use of a text-to-text model approach is useful since it allows to produce multiple ranked hierarchical predictions, while separating between the probability score for the attributes and values. this introduces further flexibility to the solution (beyond the scope of the above guidelines) by allowing to report high impacting attributes even if we are uncertain about their expected attribute values. furthermore, in comparison to approaches such as named entity recognition (ner), a text-to-text model does not require the reported top attribute values to exist in the input title. this is useful since sellers are not always mentioning the most valuable attribute values in the listing title. last, from an empirical standpoint, the text-to-text models we trained almost always outperformed models from other approaches (see section 4.2). to conclude, in this work we suggest a scalable and automatic method for using listing titles to identify the most valuable set of attribute-value pairs by learning from the buyers' filtering behavior. in the next section we describe related work in the field of attribute-value extraction and hierarchical classification tasks. in the following section we describe our data collection methodology and the training procedures used for the four models that we trained. this is followed by a quantitative comparison of the results of the models, and a qualitative evaluation of the results of our best performing one. we conclude by discussing the tradeoffs of our current approach, and describe our plans for future work. ## related work various methods are used to automatically extract attribute-value pairs from product-related text. this ranges from manual rules and regular expressions @xcite to more advanced modern learning algorithms @xcite @xcite @xcite @xcite @xcite . in contrast to our work, these methods are focusing on extracting the most complete set of attribute-value pairs, or limited to only attribute values which appear explicitly in the product-related text. apart from @xcite , non of these works have leveraged data from historical user interaction with the attribute-value pairs. hierarchical classification has been of wide interest both in computer vision applications and text related tasks. early work has been focusing on flattening the labels @xcite or on training multiple local classifiers, where the number of classifiers is dependent on the depth of the label hierarchy @xcite @xcite . more recent studies aimed to train a single neural network which can learn the label hierarchy complexity @xcite @xcite , while others combined both a single global network and multiple local classifiers @xcite . most recently, several works demonstrated that sequenceto-sequence (seq2seq) networks are a promising representation for hierarchical text classification tasks @xcite . however, less focus was given to using seq2seq for the ranking of multiple hierarchical label data structures, which are commonly being used, especially in online marketplaces. ## conclusion in this paper we demonstrate using filtering behavior data to predict the most relevant listing attribute-value pairs, and the superiority of using a text-to-text approach for modeling a hierarchical multi-label text classification (hmltc) task that combines ranking. we identify several key advantages of this solution framework: first, acquiring the training data we use is a scalable and inexpensive process which does not require manual labor. therefore, the volume of data collected in high-volume websites is likely to be sufficient for training deep-learning-based models such as seq2seq. second, unlike methods such as ner, using a text-to-text approach enables to identify attribute-value pairs that do not necessarily exist in the title, to extract multiple values per attribute ( as for classifiers performance, the seq2seq models provided better results for most metrics compared to bert and ulmfit. unlike the latter two, the seq2seq models didn't use a transfer learning approach that leverages a pre-trained language models. we suspect that the relatively short length of listing titles (12 tokens on average), combined with the unique jargon in ebay's data, which is hard to fully capture in the fine-tune process, might have negatively impacted the performance of bert and ulmfit. regardless to the classifier of choice, we keep in mind that the model's attribute ranking is clearly affected by the set of filtering options that were presented to the buyers on the site, and thus, cannot find attribute pairs that have not been historically used for filtering. therefore, to avoid a closed feedback loop scenario, we would avoid using the model's attribute ranking results as an input to decide these filtering options. also, to further increase the quality of the attribute ranking we can use a training data that consists of a sample of buyers that were served with a random (or partly random) list of filtering options. nonetheless, even without this sample, the model can still provide sellers with meaningful information about their potential buyers' current attribute priority ranking.