Compositional Semantics Network With Multi-Task Learning for Pun Location
نویسندگان
چکیده
منابع مشابه
Identification of Homographic Pun Location for Pun Understanding
This paper introduces a novel framework for homographic pun location identification. Two observations, the existence of the support term and the preferred positions of the pun in context, are considered as crucial hints for pun identification. We first nominate the pun candidates, and then select the most probable one based on various strategies. Experimental results show the effectiveness of o...
متن کاملMulti-Step Regression Learning for Compositional Distributional Semantics
We present a model for compositional distributional semantics related to the framework of Coecke et al. (2010), and emulating formal semantics by representing functions as tensors and arguments as vectors. We introduce a new learning method for tensors, generalising the approach of Baroni and Zamparelli (2010). We evaluate it on two benchmark data sets, and find it to outperform existing leadin...
متن کاملLearning Dependency-Based Compositional Semantics
Suppose we want to build a system that answers a natural language question by representing its semantics as a logical form and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this t...
متن کاملRecurrent Neural Network for Text Classification with Multi-Task Learning
Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multitask learning framework to jointly learn across multiple related tasks. Based on recurrent neural network...
متن کاملMulti-Task Metric Learning on Network Data
Multi-task learning (MTL) has been shown to improve prediction performance in a number of different contexts by learning models jointly on multiple different, but related tasks. Network data, which are a priori data with a rich relational structure, provide an important context for applying MTL. In particular, the explicit relational structure implies that network data is not i.i.d. data. Netwo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2978208