129,418 research outputs found
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!
Argumentation mining (AM) requires the identification of complex discourse
structures and has lately been applied with success monolingually. In this
work, we show that the existing resources are, however, not adequate for
assessing cross-lingual AM, due to their heterogeneity or lack of complexity.
We therefore create suitable parallel corpora by (human and machine)
translating a popular AM dataset consisting of persuasive student essays into
German, French, Spanish, and Chinese. We then compare (i) annotation projection
and (ii) bilingual word embeddings based direct transfer strategies for
cross-lingual AM, finding that the former performs considerably better and
almost eliminates the loss from cross-lingual transfer. Moreover, we find that
annotation projection works equally well when using either costly human or
cheap machine translations. Our code and data are available at
\url{http://github.com/UKPLab/coling2018-xling_argument_mining}.Comment: Accepted at Coling 201
FLASH: Randomized Algorithms Accelerated over CPU-GPU for Ultra-High Dimensional Similarity Search
We present FLASH (\textbf{F}ast \textbf{L}SH \textbf{A}lgorithm for
\textbf{S}imilarity search accelerated with \textbf{H}PC), a similarity search
system for ultra-high dimensional datasets on a single machine, that does not
require similarity computations and is tailored for high-performance computing
platforms. By leveraging a LSH style randomized indexing procedure and
combining it with several principled techniques, such as reservoir sampling,
recent advances in one-pass minwise hashing, and count based estimations, we
reduce the computational and parallelization costs of similarity search, while
retaining sound theoretical guarantees.
We evaluate FLASH on several real, high-dimensional datasets from different
domains, including text, malicious URL, click-through prediction, social
networks, etc. Our experiments shed new light on the difficulties associated
with datasets having several million dimensions. Current state-of-the-art
implementations either fail on the presented scale or are orders of magnitude
slower than FLASH. FLASH is capable of computing an approximate k-NN graph,
from scratch, over the full webspam dataset (1.3 billion nonzeros) in less than
10 seconds. Computing a full k-NN graph in less than 10 seconds on the webspam
dataset, using brute-force (), will require at least 20 teraflops. We
provide CPU and GPU implementations of FLASH for replicability of our results
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