8,911 research outputs found
Deep Binary Reconstruction for Cross-modal Hashing
With the increasing demand of massive multimodal data storage and
organization, cross-modal retrieval based on hashing technique has drawn much
attention nowadays. It takes the binary codes of one modality as the query to
retrieve the relevant hashing codes of another modality. However, the existing
binary constraint makes it difficult to find the optimal cross-modal hashing
function. Most approaches choose to relax the constraint and perform
thresholding strategy on the real-value representation instead of directly
solving the original objective. In this paper, we first provide a concrete
analysis about the effectiveness of multimodal networks in preserving the
inter- and intra-modal consistency. Based on the analysis, we provide a
so-called Deep Binary Reconstruction (DBRC) network that can directly learn the
binary hashing codes in an unsupervised fashion. The superiority comes from a
proposed simple but efficient activation function, named as Adaptive Tanh
(ATanh). The ATanh function can adaptively learn the binary codes and be
trained via back-propagation. Extensive experiments on three benchmark datasets
demonstrate that DBRC outperforms several state-of-the-art methods in both
image2text and text2image retrieval task.Comment: 8 pages, 5 figures, accepted by ACM Multimedia 201
A Mention-Ranking Model for Abstract Anaphora Resolution
Resolving abstract anaphora is an important, but difficult task for text
understanding. Yet, with recent advances in representation learning this task
becomes a more tangible aim. A central property of abstract anaphora is that it
establishes a relation between the anaphor embedded in the anaphoric sentence
and its (typically non-nominal) antecedent. We propose a mention-ranking model
that learns how abstract anaphors relate to their antecedents with an
LSTM-Siamese Net. We overcome the lack of training data by generating
artificial anaphoric sentence--antecedent pairs. Our model outperforms
state-of-the-art results on shell noun resolution. We also report first
benchmark results on an abstract anaphora subset of the ARRAU corpus. This
corpus presents a greater challenge due to a mixture of nominal and pronominal
anaphors and a greater range of confounders. We found model variants that
outperform the baselines for nominal anaphors, without training on individual
anaphor data, but still lag behind for pronominal anaphors. Our model selects
syntactically plausible candidates and -- if disregarding syntax --
discriminates candidates using deeper features.Comment: In Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing (EMNLP). Copenhagen, Denmar
DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries
In scientific research, the ability to effectively retrieve relevant
documents based on complex, multifaceted queries is critical. Existing
evaluation datasets for this task are limited, primarily due to the high cost
and effort required to annotate resources that effectively represent complex
queries. To address this, we propose a novel task, Scientific DOcument
Retrieval using Multi-level Aspect-based quEries (DORIS-MAE), which is designed
to handle the complex nature of user queries in scientific research. We
developed a benchmark dataset within the field of computer science, consisting
of 100 human-authored complex query cases. For each complex query, we assembled
a collection of 100 relevant documents and produced annotated relevance scores
for ranking them. Recognizing the significant labor of expert annotation, we
also introduce Anno-GPT, a scalable framework for validating the performance of
Large Language Models (LLMs) on expert-level dataset annotation tasks. LLM
annotation of the DORIS-MAE dataset resulted in a 500x reduction in cost,
without compromising quality. Furthermore, due to the multi-tiered structure of
these complex queries, the DORIS-MAE dataset can be extended to over 4,000
sub-query test cases without requiring additional annotation. We evaluated 17
recent retrieval methods on DORIS-MAE, observing notable performance drops
compared to traditional datasets. This highlights the need for better
approaches to handle complex, multifaceted queries in scientific research. Our
dataset and codebase are available at
https://github.com/Real-Doris-Mae/Doris-Mae-Dataset.Comment: To appear in NeurIPS 2023 Datasets and Benchmarks Trac
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