4 research outputs found
Learning Nonsymmetric Determinantal Point Processes
Determinantal point processes (DPPs) have attracted substantial attention as
an elegant probabilistic model that captures the balance between quality and
diversity within sets. DPPs are conventionally parameterized by a positive
semi-definite kernel matrix, and this symmetric kernel encodes only repulsive
interactions between items. These so-called symmetric DPPs have significant
expressive power, and have been successfully applied to a variety of machine
learning tasks, including recommendation systems, information retrieval, and
automatic summarization, among many others. Efficient algorithms for learning
symmetric DPPs and sampling from these models have been reasonably well
studied. However, relatively little attention has been given to nonsymmetric
DPPs, which relax the symmetric constraint on the kernel. Nonsymmetric DPPs
allow for both repulsive and attractive item interactions, which can
significantly improve modeling power, resulting in a model that may better fit
for some applications. We present a method that enables a tractable algorithm,
based on maximum likelihood estimation, for learning nonsymmetric DPPs from
data composed of observed subsets. Our method imposes a particular
decomposition of the nonsymmetric kernel that enables such tractable learning
algorithms, which we analyze both theoretically and experimentally. We evaluate
our model on synthetic and real-world datasets, demonstrating improved
predictive performance compared to symmetric DPPs, which have previously shown
strong performance on modeling tasks associated with these datasets.Comment: NeurIPS 201
Embedding models for recommendation under contextual constraints
Embedding models, which learn latent representations of users and items based
on user-item interaction patterns, are a key component of recommendation
systems. In many applications, contextual constraints need to be applied to
refine recommendations, e.g. when a user specifies a price range or product
category filter. The conventional approach, for both context-aware and standard
models, is to retrieve items and apply the constraints as independent
operations. The order in which these two steps are executed can induce
significant problems. For example, applying constraints a posteriori can result
in incomplete recommendations or low-quality results for the tail of the
distribution (i.e., less popular items). As a result, the additional
information that the constraint brings about user intent may not be accurately
captured.
In this paper we propose integrating the information provided by the
contextual constraint into the similarity computation, by merging constraint
application and retrieval into one operation in the embedding space. This
technique allows us to generate high-quality recommendations for the specified
constraint. Our approach learns constraints representations jointly with the
user and item embeddings. We incorporate our methods into a matrix
factorization model, and perform an experimental evaluation on one internal and
two real-world datasets. Our results show significant improvements in
predictive performance compared to context-aware and standard models
Open Domain Question Answering over Tables via Dense Retrieval
Recent advances in open-domain QA have led to strong models based on dense
retrieval, but only focused on retrieving textual passages. In this work, we
tackle open-domain QA over tables for the first time, and show that retrieval
can be improved by a retriever designed to handle tabular context. We present
an effective pre-training procedure for our retriever and improve retrieval
quality with mined hard negatives. As relevant datasets are missing, we extract
a subset of Natural Questions (Kwiatkowski et al., 2019) into a Table QA
dataset. We find that our retriever improves retrieval results from 72.0 to
81.1 recall@10 and end-to-end QA results from 33.8 to 37.7 exact match, over a
BERT based retriever.Comment: NAACL 2021 camera read
Understanding tables with intermediate pre-training
Table entailment, the binary classification task of finding if a sentence is
supported or refuted by the content of a table, requires parsing language and
table structure as well as numerical and discrete reasoning. While there is
extensive work on textual entailment, table entailment is less well studied. We
adapt TAPAS (Herzig et al., 2020), a table-based BERT model, to recognize
entailment. Motivated by the benefits of data augmentation, we create a
balanced dataset of millions of automatically created training examples which
are learned in an intermediate step prior to fine-tuning. This new data is not
only useful for table entailment, but also for SQA (Iyyer et al., 2017), a
sequential table QA task. To be able to use long examples as input of BERT
models, we evaluate table pruning techniques as a pre-processing step to
drastically improve the training and prediction efficiency at a moderate drop
in accuracy. The different methods set the new state-of-the-art on the TabFact
(Chen et al., 2020) and SQA datasets.Comment: Accepted to EMNLP Findings 202