TY  - JOUR
A1  - Afantenos, Stergos
A1  - Peldszus, Andreas
A1  - Stede, Manfred
T1  - Comparing decoding mechanisms for parsing argumentative structures
JF  - Argument & Computation
N2  - Parsing of argumentative structures has become a very active line of research in recent years. Like discourse parsing or any other natural language task that requires prediction of linguistic structures, most approaches choose to learn a local model and then perform global decoding over the local probability distributions, often imposing constraints that are specific to the task at hand. Specifically for argumentation parsing, two decoding approaches have been recently proposed: Minimum Spanning Trees (MST) and Integer Linear Programming (ILP), following similar trends in discourse parsing. In contrast to discourse parsing though, where trees are not always used as underlying annotation schemes, argumentation structures so far have always been represented with trees. Using the ‘argumentative microtext corpus’ [in: Argumentation and Reasoned Action: Proceedings of the 1st European Conference on Argumentation, Lisbon 2015 / Vol. 2, College Publications, London, 2016, pp. 801–815] as underlying data and replicating three different decoding mechanisms, in this paper we propose a novel ILP decoder and an extension to our earlier MST work, and then thoroughly compare the approaches. The result is that our new decoder outperforms related work in important respects, and that in general, ILP and MST yield very similar performance.
KW  - Argumentation structure
KW  - argument mining
KW  - parsing
Y1  - 2018
U6  - https://doi.org/10.3233/AAC-180033
SN  - 1946-2166
SN  - 1946-2174
VL  - 9
IS  - 3
SP  - 177
EP  - 192
PB  - IOS Press
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - AbuJarour, Mohammed
T1  - Information integration in services computing
Y1  - 2010
SN  - 978-3-86956-036-6
ER  - 
TY  - JOUR
A1  - Abdelwahab, Ahmed
A1  - Landwehr, Niels
T1  - Deep Distributional Sequence Embeddings Based on a Wasserstein Loss
JF  - Neural processing letters
N2  - Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors. This paper studies deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence. The motivation for this is to better capture statistical information about the distribution of patterns within the sequence in the embedding. When embeddings are distributions rather than vectors, measuring distances between embeddings involves comparing their respective distributions. The paper therefore proposes a distance metric based on Wasserstein distances between the distributions and a corresponding loss function for metric learning, which leads to a novel end-to-end trainable embedding model. We empirically observe that distributional embeddings outperform standard vector embeddings and that training with the proposed Wasserstein metric outperforms training with other distance functions.
KW  - Metric learning
KW  - Sequence embeddings
KW  - Deep learning
Y1  - 2022
U6  - https://doi.org/10.1007/s11063-022-10784-y
SN  - 1370-4621
SN  - 1573-773X
PB  - Springer
CY  - Dordrecht
ER  -