113 research outputs found
Metric Monocular Localization Using Signed Distance Fields
Metric localization plays a critical role in vision-based navigation. For
overcoming the degradation of matching photometry under appearance changes,
recent research resorted to introducing geometry constraints of the prior scene
structure. In this paper, we present a metric localization method for the
monocular camera, using the Signed Distance Field (SDF) as a global map
representation. Leveraging the volumetric distance information from SDFs, we
aim to relax the assumption of an accurate structure from the local Bundle
Adjustment (BA) in previous methods. By tightly coupling the distance factor
with temporal visual constraints, our system corrects the odometry drift and
jointly optimizes global camera poses with the local structure. We validate the
proposed approach on both indoor and outdoor public datasets. Compared to the
state-of-the-art methods, it achieves a comparable performance with a minimal
sensor configuration.Comment: Accepted to 2019 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
Mining Implicit Relevance Feedback from User Behavior for Web Question Answering
Training and refreshing a web-scale Question Answering (QA) system for a
multi-lingual commercial search engine often requires a huge amount of training
examples. One principled idea is to mine implicit relevance feedback from user
behavior recorded in search engine logs. All previous works on mining implicit
relevance feedback target at relevance of web documents rather than passages.
Due to several unique characteristics of QA tasks, the existing user behavior
models for web documents cannot be applied to infer passage relevance. In this
paper, we make the first study to explore the correlation between user behavior
and passage relevance, and propose a novel approach for mining training data
for Web QA. We conduct extensive experiments on four test datasets and the
results show our approach significantly improves the accuracy of passage
ranking without extra human labeled data. In practice, this work has proved
effective to substantially reduce the human labeling cost for the QA service in
a global commercial search engine, especially for languages with low resources.
Our techniques have been deployed in multi-language services.Comment: Accepted by KDD 202
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks
We present Unicoder, a universal language encoder that is insensitive to
different languages. Given an arbitrary NLP task, a model can be trained with
Unicoder using training data in one language and directly applied to inputs of
the same task in other languages. Comparing to similar efforts such as
Multilingual BERT and XLM, three new cross-lingual pre-training tasks are
proposed, including cross-lingual word recovery, cross-lingual paraphrase
classification and cross-lingual masked language model. These tasks help
Unicoder learn the mappings among different languages from more perspectives.
We also find that doing fine-tuning on multiple languages together can bring
further improvement. Experiments are performed on two tasks: cross-lingual
natural language inference (XNLI) and cross-lingual question answering (XQA),
where XLM is our baseline. On XNLI, 1.8% averaged accuracy improvement (on 15
languages) is obtained. On XQA, which is a new cross-lingual dataset built by
us, 5.5% averaged accuracy improvement (on French and German) is obtained.Comment: Accepted to EMNLP2019; 10 pages, 2 figure
Not All Metrics Are Guilty: Improving NLG Evaluation with LLM Paraphrasing
Most research about natural language generation (NLG) relies on evaluation
benchmarks with limited references for a sample, which may result in poor
correlations with human judgements. The underlying reason is that one semantic
meaning can actually be expressed in different forms, and the evaluation with a
single or few references may not accurately reflect the quality of the model's
hypotheses. To address this issue, this paper presents a novel method, named
Para-Ref, to enhance existing evaluation benchmarks by enriching the number of
references. We leverage large language models (LLMs) to paraphrase a single
reference into multiple high-quality ones in diverse expressions. Experimental
results on representative NLG tasks of machine translation, text summarization,
and image caption demonstrate that our method can effectively improve the
correlation with human evaluation for sixteen automatic evaluation metrics by
+7.82% in ratio. We release the code and data at
https://github.com/RUCAIBox/Para-Ref
SPatiotemporal-ENcoded acoustic radiation force imaging of focused ultrasound
Neuromodulation technology has provided novel therapeutic approaches for diseases caused by neural circuit dysfunction. Transcranial focused ultrasound (FU) is an emerging neuromodulation approach that combines noninvasiveness with relatively sharp focus, even in deep brain regions. It has numerous advantages such as high precision and good safety in neuromodulation, allowing for modulation of both peripheral and central nervous systems. To ensure accurate treatment targeting in FU neuromodulation, a magnetic resonance acoustic radiation force imaging (MR-ARFI) sequence is crucial for the visualization of the focal point. Currently, the commonly used 2D Spin Echo ARFI (2D SE-ARFI) sequence suffers from the long acquisition time, while the echo planar imaging ARFI (EPI-ARFI) sequence with a shorter acquisition time is vulnerable to the magnetic field inhomogeneities. To address these problems, we proposed a spatiotemporal-encoded acoustic radiation force imaging sequence (i.e., SE-SPEN-ARFI, shortened to SPEN-ARFI) in this study. The displacement at the focal spot obtained was highly consistent with that of the SE-ARFI sequence. Our research shows that SPEN-ARFI allows for rapid image acquisition and has less image distortions even under great field inhomogeneities. Therefore, a SPEN-ARFI sequence is a practical alternative for the treatment planning in ultrasound neuromodulation
HanoiT: Enhancing Context-aware Translation via Selective Context
Context-aware neural machine translation aims to use the document-level
context to improve translation quality. However, not all words in the context
are helpful. The irrelevant or trivial words may bring some noise and distract
the model from learning the relationship between the current sentence and the
auxiliary context. To mitigate this problem, we propose a novel end-to-end
encoder-decoder model with a layer-wise selection mechanism to sift and refine
the long document context. To verify the effectiveness of our method, extensive
experiments and extra quantitative analysis are conducted on four
document-level machine translation benchmarks. The experimental results
demonstrate that our model significantly outperforms previous models on all
datasets via the soft selection mechanism
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation
Standard automatic metrics, e.g. BLEU, are not reliable for document-level MT evaluation. They can neither distinguish document-level improvements in translation quality from sentence-level ones, nor identify the discourse phenomena that cause context-agnostic translations. This paper introduces a novel automatic metric BlonDe to widen the scope of automatic MT evaluation from sentence to document level. BlonDe takes discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans. We conduct extensive comparisons on a newly constructed dataset BWB. The experimental results show that BlonDe possesses better selectivity and interpretability at the document-level, and is more sensitive to document-level nuances. In a large-scale human study, BlonDe also achieves significantly higher Pearson’s r correlation with human judgments compared to previous metrics
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