2,124 research outputs found
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
We introduce a model for bidirectional retrieval of images and sentences
through a multi-modal embedding of visual and natural language data. Unlike
previous models that directly map images or sentences into a common embedding
space, our model works on a finer level and embeds fragments of images
(objects) and fragments of sentences (typed dependency tree relations) into a
common space. In addition to a ranking objective seen in previous work, this
allows us to add a new fragment alignment objective that learns to directly
associate these fragments across modalities. Extensive experimental evaluation
shows that reasoning on both the global level of images and sentences and the
finer level of their respective fragments significantly improves performance on
image-sentence retrieval tasks. Additionally, our model provides interpretable
predictions since the inferred inter-modal fragment alignment is explicit
On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow
Abundant data is the key to successful machine learning. However, supervised
learning requires annotated data that are often hard to obtain. In a
classification task with limited resources, Active Learning (AL) promises to
guide annotators to examples that bring the most value for a classifier. AL can
be successfully combined with self-training, i.e., extending a training set
with the unlabelled examples for which a classifier is the most certain. We
report our experiences on using AL in a systematic manner to train an SVM
classifier for Stack Overflow posts discussing performance of software
components. We show that the training examples deemed as the most valuable to
the classifier are also the most difficult for humans to annotate. Despite
carefully evolved annotation criteria, we report low inter-rater agreement, but
we also propose mitigation strategies. Finally, based on one annotator's work,
we show that self-training can improve the classification accuracy. We conclude
the paper by discussing implication for future text miners aspiring to use AL
and self-training.Comment: Preprint of paper accepted for the Proc. of the 21st International
Conference on Evaluation and Assessment in Software Engineering, 201
What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision
We present a novel method for aligning a sequence of instructions to a video
of someone carrying out a task. In particular, we focus on the cooking domain,
where the instructions correspond to the recipe. Our technique relies on an HMM
to align the recipe steps to the (automatically generated) speech transcript.
We then refine this alignment using a state-of-the-art visual food detector,
based on a deep convolutional neural network. We show that our technique
outperforms simpler techniques based on keyword spotting. It also enables
interesting applications, such as automatically illustrating recipes with
keyframes, and searching within a video for events of interest.Comment: To appear in NAACL 201
Detecting (Un)Important Content for Single-Document News Summarization
We present a robust approach for detecting intrinsic sentence importance in
news, by training on two corpora of document-summary pairs. When used for
single-document summarization, our approach, combined with the "beginning of
document" heuristic, outperforms a state-of-the-art summarizer and the
beginning-of-article baseline in both automatic and manual evaluations. These
results represent an important advance because in the absence of cross-document
repetition, single document summarizers for news have not been able to
consistently outperform the strong beginning-of-article baseline.Comment: Accepted By EACL 201
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