17,991 research outputs found
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Most recent approaches use the sequence-to-sequence model for paraphrase
generation. The existing sequence-to-sequence model tends to memorize the words
and the patterns in the training dataset instead of learning the meaning of the
words. Therefore, the generated sentences are often grammatically correct but
semantically improper. In this work, we introduce a novel model based on the
encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our
proposed model generates the words by querying distributed word representations
(i.e. neural word embeddings), hoping to capturing the meaning of the according
words. Following previous work, we evaluate our model on two
paraphrase-oriented tasks, namely text simplification and short text
abstractive summarization. Experimental results show that our model outperforms
the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two
English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a
Chinese summarization dataset. Moreover, our model achieves state-of-the-art
performances on these three benchmark datasets.Comment: arXiv admin note: text overlap with arXiv:1710.0231
Strangeness hyperon-nucleon scattering in covariant chiral effective field theory
Motivated by the successes of covariant baryon chiral perturbation theory in
one-baryon systems and in heavy-light systems, we study relevance of
relativistic effects in hyperon-nucleon interactions with strangeness .
In this exploratory work, we follow the covariant framework developed by
Epelbaum and Gegelia to calculate the scattering amplitude at leading
order. By fitting the five low-energy constants to the experimental data, we
find that the cutoff dependence is mitigated, compared with the heavy-baryon
approach. Nevertheless, the description of the experimental data remains
quantitatively similar at leading order.Comment: The manuscript has been largely rewritten but the results remain
unchanged. To appear in Physical Review
Designing Fully Distributed Consensus Protocols for Linear Multi-agent Systems with Directed Graphs
This paper addresses the distributed consensus protocol design problem for
multi-agent systems with general linear dynamics and directed communication
graphs. Existing works usually design consensus protocols using the smallest
real part of the nonzero eigenvalues of the Laplacian matrix associated with
the communication graph, which however is global information. In this paper,
based on only the agent dynamics and the relative states of neighboring agents,
a distributed adaptive consensus protocol is designed to achieve
leader-follower consensus for any communication graph containing a directed
spanning tree with the leader as the root node. The proposed adaptive protocol
is independent of any global information of the communication graph and thereby
is fully distributed. Extensions to the case with multiple leaders are further
studied.Comment: 16 page, 3 figures. To appear in IEEE Transactions on Automatic
Contro
- …