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@TECHREPORT{Pilault_Idiap-RR-27-2017,
         author = {Pilault, Jonathan and Pappas, Nikolaos and Miculicich, Lesly and Popescu-Belis, Andrei},
       projects = {Idiap, SUMMA},
          month = {9},
          title = {Evaluating Attention Networks for Anaphora Resolution},
           type = {Idiap-RR},
         number = {Idiap-RR-27-2017},
           year = {2017},
    institution = {Idiap},
           note = {Work done during an internship of the first author at the Idiap Research Institute from March to August 2017.},
       abstract = {In this paper, we evaluate the results of using inter and intra attention mechanisms
from two architectures, a Deep Attention Long Short-Term Memory-Network (LSTM-N)
(Cheng et al., 2016) and a Decomposable Attention model (Parikh et al., 2016), for
anaphora resolution, i.e. detecting coreference relations between a pronoun and a noun
(its antecedent). The models are adapted from an entailment task, to address the
pronominal coreference resolution task by comparing two pairs of sentences: one with
the original sentences containing the antecedent and the pronoun, and another one with
the pronoun replaced with a correct or an incorrect antecedent. The goal is thus to
detect the correct replacements, assuming the original sentence pair entails the one
with the correct replacement, but not one with an incorrect replacement. We use the
CoNLL-2012 English dataset (Pradhan et al., 2012) to train the models and evaluate
the ability to recognize correct and incorrect pronoun replacements in sentence pairs.
We find that the Decomposable Attention Model performs better, while using a much
simpler architecture. Furthermore, we focus on two previous studies that use intra- and
inter-attention mechanisms, discuss how they relate to each other, and examine how
these advances work to identify correct antecedent replacements.},
            pdf = {https://publications.idiap.ch/attachments/reports/2017/Pilault_Idiap-RR-27-2017.pdf}
}