991 research outputs found
When is multitask learning effective? Semantic sequence prediction under varying data conditions
Multitask learning has been applied successfully to a range of tasks, mostly
morphosyntactic. However, little is known on when MTL works and whether there
are data characteristics that help to determine its success. In this paper we
evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine
different auxiliary tasks, amongst which a novel setup, and correlate their
impact to data-dependent conditions. Our results show that MTL is not always
effective, significant improvements are obtained only for 1 out of 5 tasks.
When successful, auxiliary tasks with compact and more uniform label
distributions are preferable.Comment: In EACL 201
Combination Strategies for Semantic Role Labeling
This paper introduces and analyzes a battery of inference models for the
problem of semantic role labeling: one based on constraint satisfaction, and
several strategies that model the inference as a meta-learning problem using
discriminative classifiers. These classifiers are developed with a rich set of
novel features that encode proposition and sentence-level information. To our
knowledge, this is the first work that: (a) performs a thorough analysis of
learning-based inference models for semantic role labeling, and (b) compares
several inference strategies in this context. We evaluate the proposed
inference strategies in the framework of the CoNLL-2005 shared task using only
automatically-generated syntactic information. The extensive experimental
evaluation and analysis indicates that all the proposed inference strategies
are successful -they all outperform the current best results reported in the
CoNLL-2005 evaluation exercise- but each of the proposed approaches has its
advantages and disadvantages. Several important traits of a state-of-the-art
SRL combination strategy emerge from this analysis: (i) individual models
should be combined at the granularity of candidate arguments rather than at the
granularity of complete solutions; (ii) the best combination strategy uses an
inference model based in learning; and (iii) the learning-based inference
benefits from max-margin classifiers and global feedback
Multi-engine machine translation by recursive sentence decomposition
In this paper, we present a novel approach to combine the outputs of multiple MT engines into a consensus translation. In contrast to previous Multi-Engine Machine
Translation (MEMT) techniques, we do not rely on word alignments of output hypotheses, but prepare the input sentence for multi-engine processing. We do this by using a recursive decomposition algorithm that produces simple chunks as input to the MT engines. A consensus translation
is produced by combining the best chunk translations, selected through majority voting, a trigram language model
score and a confidence score assigned to each MT engine. We report statistically significant relative improvements
of up to 9% BLEU score in experiments (English→Spanish) carried out on an 800-sentence test set extracted from the Penn-II Treebank
Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking
Chinese geographic re-ranking task aims to find the most relevant addresses
among retrieved candidates, which is crucial for location-related services such
as navigation maps. Unlike the general sentences, geographic contexts are
closely intertwined with geographical concepts, from general spans (e.g.,
province) to specific spans (e.g., road). Given this feature, we propose an
innovative framework, namely Geo-Encoder, to more effectively integrate Chinese
geographical semantics into re-ranking pipelines. Our methodology begins by
employing off-the-shelf tools to associate text with geographical spans,
treating them as chunking units. Then, we present a multi-task learning module
to simultaneously acquire an effective attention matrix that determines chunk
contributions to extra semantic representations. Furthermore, we put forth an
asynchronous update mechanism for the proposed addition task, aiming to guide
the model capable of effectively focusing on specific chunks. Experiments on
two distinct Chinese geographic re-ranking datasets, show that the Geo-Encoder
achieves significant improvements when compared to state-of-the-art baselines.
Notably, it leads to a substantial improvement in the Hit@1 score of MGEO-BERT,
increasing it by 6.22% from 62.76 to 68.98 on the GeoTES dataset
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