Papers with pointer network
Improving Multi-turn Dialogue Modelling with Utterance ReWriter (P19-1)
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| Challenge: | Recent research has achieved impressive results in single-turn dialogue modelling, but multi-turn models still remain challenging. |
| Approach: | They propose to rewrite human utterances as a pre-process to help multi-turn dialgoue modelling. |
| Outcome: | The proposed architecture achieves remarkably good performance on the utterance rewriting task. |
The Concordia NLG Surface Realizer at SRST 2019 (D19-63)
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| Challenge: | The goal of Natural Language Generation (NLG) is to produce natural texts given structured data. |
| Approach: | They propose a model for the shallow track of the 2019 NLG Surface Realization Shared Task . they divided the problem into two sub-problems: reordering and inflecting . |
| Outcome: | The proposed model reconstructs sentences whose word order and word inflections were removed. |
A Copy-Augmented Generative Model for Open-Domain Question Answering (2022.acl-short)
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| Challenge: | Existing open-domain question answering approaches follow a two-stage paradigm retriever then reader. |
| Approach: | They propose a novel reader-based generative approach that incorporates extractive and generative readers. |
| Outcome: | The proposed model improves on two benchmark datasets, Natural Questions and TriviaQA. |
Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention (P18-2)
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| Challenge: | Experimental results show the effectiveness of our slot filling model at addressing the OOV problem. |
| Approach: | They propose a generative neural network model for slot filling based on a sequence-to-sequence model and a pointer network. |
| Outcome: | The proposed model is able to predict slot values on spoken language data. |
An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking (P18-1)
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| Challenge: | a dialogue state tracker is a core component in most of today's spoken dialogue systems . slot-filling dialogues are composed of a predefined set of slots that need to be filled through the conversation . |
| Approach: | They propose an E2E architecture that extracts unknown slot values while still achieving state-of-the-art accuracy on the standard DSTC2 benchmark. |
| Outcome: | The proposed architecture achieves state-of-the-art accuracy on the DSTC2 benchmark while retaining predefined slot values. |
Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems (P18-1)
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| Challenge: | End-to-end task-oriented dialog systems often suffer from the challenge of incorporating knowledge bases. |
| Approach: | They propose a novel yet simple end-to-end differentiable model called memory-tosequence to address this issue. |
| Outcome: | The proposed model can be trained faster and achieve state-of-the-art performance on three different task-oriented dialog datasets. |
Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks (2022.acl-long)
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| Challenge: | Constituency parsing and nested named entity recognition (NER) are similar tasks since they aim to predict a collection of nesting and non-crossing spans. |
| Approach: | They propose a model that uses a pointer network to predict a constituency tree's boundary . constituency parsing is an important task in natural language processing . |
| Outcome: | The proposed model achieves state-of-the-art performance on PTB among all BERT-based models and competitive performance on CTB7 in constituency parsing. |
Joint Multi-Decoder Framework with Hierarchical Pointer Network for Frame Semantic Parsing (2021.findings-acl)
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| Challenge: | Current researches on frame semantic parsing ignore the interactions among subtasks. |
| Approach: | They propose a multi-decoder strategy to handle these subtasks together . they propose introducing a hierarchical pointer network for argument identification . |
| Outcome: | The proposed architecture improves on state-of-the-art models on FrameNet dataset. |
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment (2021.acl-long)
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| Challenge: | Experimental results show that denoising word alignment improves cross-lingual transferability . most applications and resources are still English-centric, making non-English users hard to access. |
| Approach: | They propose to denoise word alignment as a cross-lingual pre-training task . they first self-label word alignments for parallel sentences and then mask tokens . |
| Outcome: | The proposed model improves cross-lingual transferability on token-level tasks, especially on question answering, and structured prediction. |
Bridge the Gap: High-level Semantic Planning for Image Captioning (2020.coling-main)
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| Challenge: | Recent image captioning models have improved the multi-modal interaction, such as attention mechanisms. |
| Approach: | They propose a high-level semantic planning mechanism that integrates a semantic reconstruction and an explicit order planning mechanism to bridge the gap between visual and language domains. |
| Outcome: | The proposed model outperforms previous methods and achieves the state-of-the-art performance on MS COCO. |
Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints (2023.findings-acl)
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| Challenge: | Existing work has not evaluated LNMT models under challenging real-world conditions. |
| Approach: | They propose a homograph disambiguation module and a model that integrates contextually rich information about unseen lexical constraints from pre-trained language models. |
| Outcome: | The proposed model can cope with “homographs” and “unseen” lexical constraints. |
Don’t Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction (2020.acl-main)
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| Challenge: | Existing aspect extraction methods suffer from boundary errors, but they hurt performance severely. |
| Approach: | They propose to use a pointer network to reposition the boundaries of extracted aspects . they conduct experiments on laptop and restaurant benchmark datasets . |
| Outcome: | The proposed method outperforms state-of-the-art methods on benchmark datasets . it achieves substantial improvements over baseline and outperformed existing methods . |
News Article Teaser Tweets and How to Generate Them (N19-1)
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| Challenge: | A teaser is a short reading suggestion for an article that is illustrative and includes curiosity-arousing elements to entice potential readers to read particular news items. |
| Approach: | They propose a benchmark and baseline system for the process of generating teasers. |
| Outcome: | The proposed system is best performing with a pointer network. |
Deep Attentive Sentence Ordering Network (D18-1)
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| Challenge: | Existing methods for sentence ordering tasks rely on linguistic knowledge and are domain specific. |
| Approach: | They propose a deep attentive sentence ordering network which integrates self-attention mechanism with LSTMs in the encoding of input sentences. |
| Outcome: | The proposed model outperforms the state-of-the-art models on Sentence Ordering and Order Discrimination tasks and is shown to be highly efficient. |
Don’t Parse, Choose Spans! Continuous and Discontinuous Constituency Parsing via Autoregressive Span Selection (2023.acl-long)
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| Challenge: | Constituency parsing is a fundamental task in natural language processing, having many applications in downstream tasks such as language modeling. |
| Approach: | They propose a simple and unified approach for both continuous and discontinuous constituency parsing via autoregressive span selection. |
| Outcome: | The proposed model can predict all possible continuous and discontinuous constituency trees without sacrificing data coverage and without expensive chart-based parsing algorithms. |
BERT-enhanced Relational Sentence Ordering Network (2020.emnlp-main)
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| Challenge: | Existing approaches to improve coherence modeling for paragraphs have been developed. |
| Approach: | They propose a BERT-enhanced Relational Sentence Ordering Network to capture better dependency relationship among sentences and exploit it with a deep relational module. |
| Outcome: | The proposed model shows significant improvement over the state-of-the-art on six datasets. |
Pointing to Select: A Fast Pointer-LSTM for Long Text Classification (2020.coling-main)
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| Challenge: | Existing methods to skip irrelevant words in text processing are slow and vanishing gradients can cause slow inference and a loss of coherence. |
| Approach: | They propose a pointer network-based LSTM framework which can change skip rates during inference. |
| Outcome: | The proposed model is 1.1x3.5x faster than the standard LSTM framework and more accurate than Leap-LSTM at high skip rates. |
Confusionset-guided Pointer Networks for Chinese Spelling Check (P19-1)
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| Challenge: | Existing methods to detect and fix errors in Chinese are limited due to context. |
| Approach: | They propose a Confusionset-guided pointer network for Chinese Spell Check task . they propose to use off-the-shelf confusionset to guide character generation . |
| Outcome: | The proposed model outperforms all competitor models on three human-annotated datasets. |
Answering Conversational Questions on Structured Data without Logical Forms (D19-1)
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| Challenge: | Existing approaches to answering sequential questions based on structured objects do not use a logical form as an intermediate representation. |
| Approach: | They propose a novel approach to answering sequential questions based on structured objects without using a logical form as an intermediate representation. |
| Outcome: | The proposed approach is competitively tested on the Sequential Question Answering (SQA) task. |
Exploring the Synergy of Dual-path Encoder and Alignment Module for Better Graph-to-Text Generation (2024.lrec-main)
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| Challenge: | KG-to-text generation model lacks explicit graph-text alignment strategy due to discrepancy between textual and structure information. |
| Approach: | They propose a synergetic knowledge graph-to-text model with a dual-path encoder, alignment module and guidance module to solve these problems. |
| Outcome: | The proposed model achieves competitive performance on three benchmark datasets. |