73 research outputs found
LambdaLoss: Metric-Driven Loss for Learning-to Rank
How to directly optimize ranking metrics such as Normalized Discounted Cumulative Gain (NDCG) is an interesting but challenging problem, because ranking metrics are either flat or discontinuous everywhere. Among existing approaches, LambdaRank is a novel algorithm that incorporates metrics into its learning procedure. Though empirically effective, it still lacks theoretical justification. For example, what is the underlying loss that LambdaRank optimizes for? Due to this, it is unclear whether LambdaRank will always converge. In this paper, we present a well-defined loss for LambdaRank in a probabilistic framework and show that LambdaRank is a special configuration in our framework. This framework, which we call LambdaLoss, provides theoretical justification for Lamb-daRank. Furthermore, we propose a few more metric-driven loss functions in our LambdaLoss framework. Our loss functions have clear connection to ranking metrics and can be optimized in our framework efficiently. Experiments on three publicly available data sets show that our methods significantly outperform the state-of-the-art learning-to-rank algorithms. This confirms both the theoretical soundness and the practical effectiveness of the LambdaLoss framework
WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
The milestone improvements brought about by deep representation learning and
pre-training techniques have led to large performance gains across downstream
NLP, IR and Vision tasks. Multimodal modeling techniques aim to leverage large
high-quality visio-linguistic datasets for learning complementary information
(across image and text modalities). In this paper, we introduce the
Wikipedia-based Image Text (WIT) Dataset
(https://github.com/google-research-datasets/wit) to better facilitate
multimodal, multilingual learning. WIT is composed of a curated set of 37.6
million entity rich image-text examples with 11.5 million unique images across
108 Wikipedia languages. Its size enables WIT to be used as a pretraining
dataset for multimodal models, as we show when applied to downstream tasks such
as image-text retrieval. WIT has four main and unique advantages. First, WIT is
the largest multimodal dataset by the number of image-text examples by 3x (at
the time of writing). Second, WIT is massively multilingual (first of its kind)
with coverage over 100+ languages (each of which has at least 12K examples) and
provides cross-lingual texts for many images. Third, WIT represents a more
diverse set of concepts and real world entities relative to what previous
datasets cover. Lastly, WIT provides a very challenging real-world test set, as
we empirically illustrate using an image-text retrieval task as an example
TRAINING A RANKING MODEL
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a ranking machine learning model. In one aspect, a method includes the actions of receiving training data for a ranking machine learning model, the training data including training examples, and each training example including data identifying: a search query, result documents from a result list for the search query, and a result document that was selected by a user from the result list, receiving position data for each training example in the training data, the position data identifying a respective position of the selected result document in the result list for the search query in the training example; determining, for each training example in the training data, a respective selection bias value; and determining a respective importance value for each training example from the selection bias value for the training example, the importance value
Exploring the Viability of Synthetic Query Generation for Relevance Prediction
Query-document relevance prediction is a critical problem in Information
Retrieval systems. This problem has increasingly been tackled using
(pretrained) transformer-based models which are finetuned using large
collections of labeled data. However, in specialized domains such as e-commerce
and healthcare, the viability of this approach is limited by the dearth of
large in-domain data. To address this paucity, recent methods leverage these
powerful models to generate high-quality task and domain-specific synthetic
data. Prior work has largely explored synthetic data generation or query
generation (QGen) for Question-Answering (QA) and binary (yes/no) relevance
prediction, where for instance, the QGen models are given a document, and
trained to generate a query relevant to that document. However in many
problems, we have a more fine-grained notion of relevance than a simple yes/no
label. Thus, in this work, we conduct a detailed study into how QGen approaches
can be leveraged for nuanced relevance prediction. We demonstrate that --
contrary to claims from prior works -- current QGen approaches fall short of
the more conventional cross-domain transfer-learning approaches. Via empirical
studies spanning 3 public e-commerce benchmarks, we identify new shortcomings
of existing QGen approaches -- including their inability to distinguish between
different grades of relevance. To address this, we introduce label-conditioned
QGen models which incorporates knowledge about the different relevance. While
our experiments demonstrate that these modifications help improve performance
of QGen techniques, we also find that QGen approaches struggle to capture the
full nuance of the relevance label space and as a result the generated queries
are not faithful to the desired relevance label.Comment: In Proceedings of ACM SIGIRWorkshop on eCommerce (SIGIR eCom 23
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