2,177 research outputs found
A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations
We introduce an attention-based Bi-LSTM for Chinese implicit discourse
relations and demonstrate that modeling argument pairs as a joint sequence can
outperform word order-agnostic approaches. Our model benefits from a partial
sampling scheme and is conceptually simple, yet achieves state-of-the-art
performance on the Chinese Discourse Treebank. We also visualize its attention
activity to illustrate the model's ability to selectively focus on the relevant
parts of an input sequence.Comment: To appear at ACL2017, code available at
https://github.com/sronnqvist/discourse-ablst
Adaptive Prompt Learning with Distilled Connective Knowledge for Implicit Discourse Relation Recognition
Implicit discourse relation recognition (IDRR) aims at recognizing the
discourse relation between two text segments without an explicit connective.
Recently, the prompt learning has just been applied to the IDRR task with great
performance improvements over various neural network-based approaches. However,
the discrete nature of the state-art-of-art prompting approach requires manual
design of templates and answers, a big hurdle for its practical applications.
In this paper, we propose a continuous version of prompt learning together with
connective knowledge distillation, called AdaptPrompt, to reduce manual design
efforts via continuous prompting while further improving performance via
knowledge transfer. In particular, we design and train a few virtual tokens to
form continuous templates and automatically select the most suitable one by
gradient search in the embedding space. We also design an answer-relation
mapping rule to generate a few virtual answers as the answer space.
Furthermore, we notice the importance of annotated connectives in the training
dataset and design a teacher-student architecture for knowledge transfer.
Experiments on the up-to-date PDTB Corpus V3.0 validate our design objectives
in terms of the better relation recognition performance over the
state-of-the-art competitors
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph
We argue that semantic meanings of a sentence or clause can not be
interpreted independently from the rest of a paragraph, or independently from
all discourse relations and the overall paragraph-level discourse structure.
With the goal of improving implicit discourse relation classification, we
introduce a paragraph-level neural networks that model inter-dependencies
between discourse units as well as discourse relation continuity and patterns,
and predict a sequence of discourse relations in a paragraph. Experimental
results show that our model outperforms the previous state-of-the-art systems
on the benchmark corpus of PDTB.Comment: Accepted by NAACL 201
PersoNER: Persian named-entity recognition
© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network
- …