Towards Domain-Independent Text Structuring Trainable on Large Discourse Treebanks (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Text structuring is a fundamental step in natural language generation, especially when generating multi-sentential text. |
| Approach: | They propose a novel task that combines neural dependency tree induction with pointer networks to train on large discourse treebanks. |
| Outcome: | The proposed method outperforms existing content ordering metrics and outperformed existing ones. |
Similar Papers
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020.acl-main)
Copied to clipboard
| Challenge: | Existing graph-based methods for text classification cannot capture contextual word relationships within each document nor can they produce inductive learning of new words. |
| Approach: | They propose to use Graph Neural Networks to learn the local word representations and then aggregate the word nodes as the document embeddings. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four benchmark datasets. |
Discourse Representation Structure Parsing (P18-1)
Copied to clipboard
| Challenge: | Existing semantic parsers are data-driven using annotated examples consisting of utterances and their meaning representations. |
| Approach: | They propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages. |
| Outcome: | The proposed model outperforms baseline models on the Groningen Meaning Bank (GMB) by a wide margin. |
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to generative commonsense reasoning hypothesize that pre-trained models lack sufficient parametric knowledge for this task. |
| Approach: | They propose to use order-agnostic input to elaborately manipulate the order of the given concepts before generation to evaluate their commonsense knowledge. |
| Outcome: | The proposed approach outperforms more sophisticated models with a lot of external data and resources in the task of generating a logical sentence from a set of concepts. |
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
Copied to clipboard
| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
| Approach: | They propose a discourse parser that incorporates recent contextual language models to improve the performance of RST-based discourse parses. |
| Outcome: | The proposed parser outperforms existing models on two key RST datasets and on large-scale "silver-standard" discourse treebank MEGA-DT. |
Discourse Representation Parsing for Sentences and Documents (P19-1)
Copied to clipboard
| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020.coling-industry)
Copied to clipboard
Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White
| Challenge: | Natural language generation (NLG) is a critical component in conversational systems . Traditionally, NLG components have been deployed using template-based solutions . however, deployment of such model-based systems has been challenging due to high latency and data needs. |
| Approach: | They propose a family of techniques to deploy data-efficient neural solutions for NLG in conversational systems to production. |
| Outcome: | The proposed techniques achieve production quality with light-weight neural network models using fraction of the data needed otherwise. |
SLM: Learning a Discourse Language Representation with Sentence Unshuffling (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent models for learning discourse language representations focus on bottom or top-level representations, but they do not capture intermediate-size structures in natural languages such as sentences and the relationships among them. |
| Approach: | They propose a new objective for learning a discourse language representation in a self-supervised manner by shuffling the sequence of input sentences and training a hierarchical transformer model to reconstruct the original ordering. |
| Outcome: | The proposed model improves the original BERT model on downstream tasks by large margins. |
Learning to Order Graph Elements with Application to Multilingual Surface Realization (D19-63)
Copied to clipboard
| Challenge: | Recent advances in deep learning have shown promises in solving combinatorial optimization problems, such as sorting variable-sized sequences. |
| Approach: | They propose an encoder-decoder framework that learns the representation for each element and predicts the ordering of each local neighborhood of the graph in turn. |
| Outcome: | The proposed framework outperforms previous frameworks on multilingual surface realization tasks while outperforming those below by a large margin. |
Evaluating Discourse in Structured Text Representations (P19-1)
Copied to clipboard
| Challenge: | Discourse structure is integral to understanding a text and is useful in many NLP tasks. |
| Approach: | They propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree. |
| Outcome: | The proposed model improves performance on multiple discourse-relevant tasks and datasets and ablation studies show it does little to capture discourse structure. |
Deep Attentive Sentence Ordering Network (D18-1)
Copied to clipboard
| 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. |