Papers with memory networks
Modeling discourse cohesion for discourse parsing via memory network (P18-2)
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| Challenge: | Existing approaches to discourse parsing focus on studying the semantic and syntactic aspects of EDU pairs, but they do not address long span dependencies. |
| Approach: | They propose a new transition-based discourse parser that takes discourse cohesion into account by using memory networks. |
| Outcome: | The proposed method outperforms traditional features and improves performance on the RST discourse treebank. |
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue (2020.lrec-1)
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| Challenge: | Current architectures only take care of semantic and contextual information for a given query and fail to fully account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. |
| Approach: | They propose a multi-stream deep learning architecture that learns unified embeddings for query-response pairs by incorporating Graph Convolution Networks over their dependency parse. |
| Outcome: | The proposed architecture improves on the next sentence prediction task and significantly improves existing techniques. |
Document Context Neural Machine Translation with Memory Networks (P18-1)
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| Challenge: | Experimental results show that our model exploits both source and target document context. |
| Approach: | They propose a document-level neural machine translation model which takes both source and target document context into account using memory networks. |
| Outcome: | The proposed model outperforms previous work in terms of BLEU and METEOR in English translations. |
Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts (N18-1)
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| Challenge: | Existing keyphrase extraction methods suffer from data sparsity problem when conducted on short and informal texts. |
| Approach: | They propose a neural keyphrase extraction framework for microblog posts that takes conversation context into account and uses four types of neural encoders to represent conversation context. |
| Outcome: | The proposed framework outperforms state-of-the-art keyphrase extraction methods on Twitter and Weibo datasets. |
Topic Memory Networks for Short Text Classification (D18-1)
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| Challenge: | Existing classification models for short texts are weak due to data sparsity . |
| Approach: | They propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. |
| Outcome: | The proposed model outperforms state-of-the-art models on short text classification, while generating coherent topics. |
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis (D18-1)
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| Challenge: | Aspect-based sentiment analysis is a new approach to extract aspect specific sentimental information from user feedback. |
| Approach: | They propose a method that incorporates neighboring aspects related information into the sentiment classification of a target aspect using memory networks. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.6% on average in restaurant and laptop domains. |
Contrastive Language Adaptation for Cross-Lingual Stance Detection (D19-1)
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| Challenge: | Current approaches to fact-checking are time-consuming and tedious. |
| Approach: | They propose a novel approach which leverages labeled data in one language to identify relative perspective of a document with respect to a claim in a different target language. |
| Outcome: | The proposed approach can deal with the challenge of limited labeled data in the target language. |
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks (D19-1)
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| Challenge: | Existing memory networks do not perform well when leveraging heterogeneous information from different sources. |
| Approach: | They propose to use user utterances, dialogue history and background knowledge tuples to integrate external knowledge into a neural dialogue model. |
| Outcome: | The proposed model outperforms the state-of-the-art data-driven task-oriented dialogue models on real-world datasets. |
Improving Chinese Word Segmentation with Wordhood Memory Networks (2020.acl-main)
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| Challenge: | Contextual features are important in Chinese word segmentation (CWS) but it is difficult to integrate wordhood information into existing neural models. |
| Approach: | They propose a neural framework that integrates contextual wordhood information with several popular encoder-decoder combinations for Chinese word segmentation. |
| Outcome: | The proposed framework achieves state-of-the-art performance on five benchmark datasets. |
A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information (2023.findings-acl)
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| Challenge: | Existing question answering methods assume that the input content can always be accessed while answering the question. |
| Approach: | They propose a model that performs rehearsal and anticipation while processing inputs to memorize important information for question answering tasks from streaming data. |
| Outcome: | The proposed model improves on short-sequence (bAbI) and large-squence textual (NarrativeQA) and video (ActivityNet-QA) question answering datasets. |