A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation

• URL: http://arxiv.org/abs/2110.07209v1
• Date: Thu, 14 Oct 2021 08:15:04 GMT
• Title: A Dual-Attention Neural Network for Pun Location and Using Pun-Gloss Pairs for Interpretation
• Authors: Shen Liu, Meirong Ma, Hao Yuan, Jianchao Zhu, Yuanbin Wu, Man Lan
• Abstract summary: Pun location is to identify the punning word in a text. Pun interpretation is to find out two different meanings of the punning word.
• Score: 25.2990606699585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pun location is to identify the punning word (usually a word or a phrase that makes the text ambiguous) in a given short text, and pun interpretation is to find out two different meanings of the punning word. Most previous studies adopt limited word senses obtained by WSD(Word Sense Disambiguation) technique or pronunciation information in isolation to address pun location. For the task of pun interpretation, related work pays attention to various WSD algorithms. In this paper, a model called DANN (Dual-Attentive Neural Network) is proposed for pun location, effectively integrates word senses and pronunciation with context information to address two kinds of pun at the same time. Furthermore, we treat pun interpretation as a classification task and construct pungloss pairs as processing data to solve this task. Experiments on the two benchmark datasets show that our proposed methods achieve new state-of-the-art results. Our source code is available in the public code repository.

Related papers

• Can Word Sense Distribution Detect Semantic Changes of Words? [35.17635565325166]
  We show that word sense distributions can be accurately used to predict semantic changes of words in English, German, Swedish and Latin. Our experimental results on SemEval 2020 Task 1 dataset show that word sense distributions can be accurately used to predict semantic changes of words.
  arXiv  Detail & Related papers  (2023-10-16T13:41:27Z)
• Context-Situated Pun Generation [42.727010784168115]
  We propose a new task, context-situated pun generation, where a specific context represented by a set of keywords is provided. The task is to first identify suitable pun words that are appropriate for the context, then generate puns based on the context keywords and the identified pun words. We show that 69% of our top retrieved pun words can be used to generate context-situated puns, and our generation module yields successful 31% of the time.
  arXiv  Detail & Related papers  (2022-10-24T18:24:48Z)
• ExPUNations: Augmenting Puns with Keywords and Explanations [88.58174386894913]
  We augment an existing dataset of puns with detailed crowdsourced annotations of keywords. This is the first humor dataset with such extensive and fine-grained annotations specifically for puns. We propose two tasks: explanation generation to aid with pun classification and keyword-conditioned pun generation.
  arXiv  Detail & Related papers  (2022-10-24T18:12:02Z)
• AmbiPun: Generating Humorous Puns with Ambiguous Context [31.81213062995652]
  Our model first produces a list of related concepts through a reverse dictionary. We then utilize one-shot GPT3 to generate context words and then generate puns incorporating context words from both concepts. Human evaluation shows that our method successfully generates pun 52% of the time.
  arXiv  Detail & Related papers  (2022-05-04T00:24:11Z)
• Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories [47.03271152494389]
  Word Sense Disambiguation aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictions on rare word senses due to limited training data. We propose a gloss alignment algorithm that can align definition sentences with the same meaning from different sense inventories to collect rich lexical knowledge.
  arXiv  Detail & Related papers  (2021-10-27T00:04:33Z)
• UCPhrase: Unsupervised Context-aware Quality Phrase Tagging [63.86606855524567]
  UCPhrase is a novel unsupervised context-aware quality phrase tagger. We induce high-quality phrase spans as silver labels from consistently co-occurring word sequences. We show that our design is superior to state-of-the-art pre-trained, unsupervised, and distantly supervised methods.
  arXiv  Detail & Related papers  (2021-05-28T19:44:24Z)
• R$^2$-Net: Relation of Relation Learning Network for Sentence Semantic Matching [58.72111690643359]
  We propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. We first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task.
  arXiv  Detail & Related papers  (2020-12-16T13:11:30Z)
• "The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition [80.59427655743092]
  We propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor. PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols. Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks.
  arXiv  Detail & Related papers  (2020-04-29T20:12:20Z)
• Decomposing Word Embedding with the Capsule Network [23.294890047230584]
  We propose a capsule network-based method to Decompose the unsupervised word Embedding of an ambiguous word into context specific Sense embedding. With attention operations, CapsDecE2S integrates the word context to reconstruct the multiple morpheme-like vectors into the context-specific sense embedding. In this method, we convert the sense learning into a binary classification that explicitly learns the relation between senses by the label of matching and non-matching.
  arXiv  Detail & Related papers  (2020-04-07T06:37:27Z)
• CASE: Context-Aware Semantic Expansion [68.30244980290742]
  This paper defines and studies a new task called Context-Aware Semantic Expansion (CASE) Given a seed term in a sentential context, we aim to suggest other terms that well fit the context as the seed. We show that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner.
  arXiv  Detail & Related papers  (2019-12-31T06:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.