109,810 research outputs found
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing
We introduce a novel method for multilingual transfer that utilizes deep
contextual embeddings, pretrained in an unsupervised fashion. While contextual
embeddings have been shown to yield richer representations of meaning compared
to their static counterparts, aligning them poses a challenge due to their
dynamic nature. To this end, we construct context-independent variants of the
original monolingual spaces and utilize their mapping to derive an alignment
for the context-dependent spaces. This mapping readily supports processing of a
target language, improving transfer by context-aware embeddings. Our
experimental results demonstrate the effectiveness of this approach for
zero-shot and few-shot learning of dependency parsing. Specifically, our method
consistently outperforms the previous state-of-the-art on 6 tested languages,
yielding an improvement of 6.8 LAS points on average.Comment: NAACL 201
Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos
We propose a new zero-shot Event Detection method by Multi-modal
Distributional Semantic embedding of videos. Our model embeds object and action
concepts as well as other available modalities from videos into a
distributional semantic space. To our knowledge, this is the first Zero-Shot
event detection model that is built on top of distributional semantics and
extends it in the following directions: (a) semantic embedding of multimodal
information in videos (with focus on the visual modalities), (b) automatically
determining relevance of concepts/attributes to a free text query, which could
be useful for other applications, and (c) retrieving videos by free text event
query (e.g., "changing a vehicle tire") based on their content. We embed videos
into a distributional semantic space and then measure the similarity between
videos and the event query in a free text form. We validated our method on the
large TRECVID MED (Multimedia Event Detection) challenge. Using only the event
title as a query, our method outperformed the state-of-the-art that uses big
descriptions from 12.6% to 13.5% with MAP metric and 0.73 to 0.83 with ROC-AUC
metric. It is also an order of magnitude faster.Comment: To appear in AAAI 201
SECaps: A Sequence Enhanced Capsule Model for Charge Prediction
Automatic charge prediction aims to predict appropriate final charges
according to the fact descriptions for a given criminal case. Automatic charge
prediction plays a critical role in assisting judges and lawyers to improve the
efficiency of legal decisions, and thus has received much attention.
Nevertheless, most existing works on automatic charge prediction perform
adequately on high-frequency charges but are not yet capable of predicting
few-shot charges with limited cases. In this paper, we propose a Sequence
Enhanced Capsule model, dubbed as SECaps model, to relieve this problem.
Specifically, following the work of capsule networks, we propose the seq-caps
layer, which considers sequence information and spatial information of legal
texts simultaneously. Then we design a attention residual unit, which provides
auxiliary information for charge prediction. In addition, our SECaps model
introduces focal loss, which relieves the problem of imbalanced charges.
Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4%
absolutely considerable improvements under Macro F1 in Criminal-S and
Criminal-L respectively. The experimental results consistently demonstrate the
superiorities and competitiveness of our proposed model.Comment: 13 pages, 3figures, 5 table
Objects2action: Classifying and localizing actions without any video example
The goal of this paper is to recognize actions in video without the need for
examples. Different from traditional zero-shot approaches we do not demand the
design and specification of attribute classifiers and class-to-attribute
mappings to allow for transfer from seen classes to unseen classes. Our key
contribution is objects2action, a semantic word embedding that is spanned by a
skip-gram model of thousands of object categories. Action labels are assigned
to an object encoding of unseen video based on a convex combination of action
and object affinities. Our semantic embedding has three main characteristics to
accommodate for the specifics of actions. First, we propose a mechanism to
exploit multiple-word descriptions of actions and objects. Second, we
incorporate the automated selection of the most responsive objects per action.
And finally, we demonstrate how to extend our zero-shot approach to the
spatio-temporal localization of actions in video. Experiments on four action
datasets demonstrate the potential of our approach
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