66 research outputs found
Entity Query Feature Expansion Using Knowledge Base Links
Recent advances in automatic entity linking and knowledge base
construction have resulted in entity annotations for document and
query collections. For example, annotations of entities from large
general purpose knowledge bases, such as Freebase and the Google
Knowledge Graph. Understanding how to leverage these entity
annotations of text to improve ad hoc document retrieval is an open
research area. Query expansion is a commonly used technique to
improve retrieval effectiveness. Most previous query expansion
approaches focus on text, mainly using unigram concepts. In this
paper, we propose a new technique, called entity query feature
expansion (EQFE) which enriches the query with features from
entities and their links to knowledge bases, including structured
attributes and text. We experiment using both explicit query entity
annotations and latent entities. We evaluate our technique on TREC
text collections automatically annotated with knowledge base entity
links, including the Google Freebase Annotations (FACC1) data.
We find that entity-based feature expansion results in significant
improvements in retrieval effectiveness over state-of-the-art text
expansion approaches
Adherence and Constancy in LIME-RS Explanations for Recommendation
Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence. In
particular, post-hoc approaches have proved to be the most easily applicable ones to increasingly complex recommendation
models, which are then treated as black boxes. The most recent literature has shown that for post-hoc explanations based
on local surrogate models, there are problems related to the robustness of the approach itself. This consideration becomes
even more relevant in human-related tasks like recommendation. The explanation also has the arduous task of enhancing
increasingly relevant aspects of user experience such as transparency or trustworthiness. This paper aims to show how
the characteristics of a classical post-hoc model based on surrogates is strongly model-dependent and does not prove to be
accountable for the explanations generatedThe authors acknowledge partial support of PID2019-108965GB-I00, PONARS01_00876BIO-D,CasadelleTecnologie
mergenti della CittĂ di Matera, PONARS01_00821FLET4.0, PIAServiziLocali2.0,H2020Passapartout-Grantn. 101016956, PIAERP4.0,andIPZS-PRJ4_IA_NORMATIV
A Multi-Task Architecture on Relevance-based Neural Query Translation
We describe a multi-task learning approach to train a Neural Machine
Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search
query translation. The translation process for Cross-lingual Information
Retrieval (CLIR) task is usually treated as a black box and it is performed as
an independent step. However, an NMT model trained on sentence-level parallel
data is not aware of the vocabulary distribution of the retrieval corpus. We
address this problem with our multi-task learning architecture that achieves
16% improvement over a strong NMT baseline on Italian-English query-document
dataset. We show using both quantitative and qualitative analysis that our
model generates balanced and precise translations with the regularization
effect it achieves from multi-task learning paradigm.Comment: Accepted for publication at ACL 201
Cross Temporal Recurrent Networks for Ranking Question Answer Pairs
Temporal gates play a significant role in modern recurrent-based neural
encoders, enabling fine-grained control over recursive compositional operations
over time. In recurrent models such as the long short-term memory (LSTM),
temporal gates control the amount of information retained or discarded over
time, not only playing an important role in influencing the learned
representations but also serving as a protection against vanishing gradients.
This paper explores the idea of learning temporal gates for sequence pairs
(question and answer), jointly influencing the learned representations in a
pairwise manner. In our approach, temporal gates are learned via 1D
convolutional layers and then subsequently cross applied across question and
answer for joint learning. Empirically, we show that this conceptually simple
sharing of temporal gates can lead to competitive performance across multiple
benchmarks. Intuitively, what our network achieves can be interpreted as
learning representations of question and answer pairs that are aware of what
each other is remembering or forgetting, i.e., pairwise temporal gating. Via
extensive experiments, we show that our proposed model achieves
state-of-the-art performance on two community-based QA datasets and competitive
performance on one factoid-based QA dataset.Comment: Accepted to AAAI201
C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations
Finding suitable citations for scientific publications can be challenging and time-consuming. To this end, context-aware citation recommendation approaches that recommend publications as candidates for in-text citations have been developed. In this paper, we present C-Rex, a web-based demonstration system available at http://c-rex.org for context-aware citation recommendation based on the Neural Citation Network [5] and millions of publications from the Microsoft Academic Graph. Our system is one of the first online context-aware citation recommendation systems and the first to incorporate not only a deep learning recommendation approach, but also explanation components to help users better understand why papers were recommended. In our offline evaluation, our model performs similarly to the one presented in the original paper and can serve as a basic framework for further implementations. In our online evaluation, we found that the explanations of recommendations increased users’ satisfaction
Personalized Search Via Neural Contextual Semantic Relevance Ranking
Existing neural relevance models do not give enough consideration for query
and item context information which diversifies the search results to adapt for
personal preference. To bridge this gap, this paper presents a neural learning
framework to personalize document ranking results by leveraging the signals to
capture how the document fits into users' context. In particular, it models the
relationships between document content and user query context using both
lexical representations and semantic embeddings such that the user's intent can
be better understood by data enrichment of personalized query context
information. Extensive experiments performed on the search dataset, demonstrate
the effectiveness of the proposed method.Comment: Contextual, Personalization, Search, Semantics, LLM, embeddin
Explaining Latent Factor Models for Recommendation with Influence Functions
Latent factor models (LFMs) such as matrix factorization achieve the
state-of-the-art performance among various Collaborative Filtering (CF)
approaches for recommendation. Despite the high recommendation accuracy of
LFMs, a critical issue to be resolved is the lack of explainability. Extensive
efforts have been made in the literature to incorporate explainability into
LFMs. However, they either rely on auxiliary information which may not be
available in practice, or fail to provide easy-to-understand explanations. In
this paper, we propose a fast influence analysis method named FIA, which
successfully enforces explicit neighbor-style explanations to LFMs with the
technique of influence functions stemmed from robust statistics. We first
describe how to employ influence functions to LFMs to deliver neighbor-style
explanations. Then we develop a novel influence computation algorithm for
matrix factorization with high efficiency. We further extend it to the more
general neural collaborative filtering and introduce an approximation algorithm
to accelerate influence analysis over neural network models. Experimental
results on real datasets demonstrate the correctness, efficiency and usefulness
of our proposed method
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