MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing meta-path generation methods cannot fully exploit rich textual information in HINs. |
| Approach: | They propose a text-infilling-based approach to generate meta-paths from textual information in HINs. |
| Outcome: | The proposed approach can classify edges in the zero-shot setting, where existing methods cannot generate meta-paths. |
Similar Papers
Dynamic Meta-Embeddings for Improved Sentence Representations (D18-1)
Copied to clipboard
| Challenge: | A sprawling literature has emerged about what word embeddings are most useful for which tasks . word embed-ding is a technique that can be used to learn word-level meaning representations for a variety of tasks. |
| Approach: | They propose a method for supervised learning of embedding ensembles that leads to state-of-the-art performance on a variety of tasks. |
| Outcome: | The proposed method leads to state-of-the-art performance on a variety of tasks. |
Meta Learning and Its Applications to Natural Language Processing (2021.acl-tutorials)
Copied to clipboard
| Challenge: | Meta-learning is a new technique that aims to learn better learning algorithms, including better parameter initialization, optimization strategy, network architecture, distance metrics, and beyond. |
| Approach: | This tutorial introduces Meta-learning approaches and the theory behind them, and then reviews the works of applying this technology to NLP problems. |
| Outcome: | This tutorial will introduce Meta-learning approaches and the theory behind them, and then review the works of applying this technology to NLP problems. |
Meta Learning for Natural Language Processing: A Survey (2022.naacl-main)
Copied to clipboard
| Challenge: | Meta-learning is an emerging field in machine learning, but there is no systematic survey of these approaches in NLP. |
| Approach: | They propose to introduce meta-learning and the common approaches and summarize their work and review their work in the NLP community. |
| Outcome: | The proposed methods improve performance in many NLP tasks but are limited to domains, languages, countries, or styles. |
MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision (2021.emnlp-main)
Copied to clipboard
| Challenge: | Sequence labeling aims to predict fine-grained sequences of labels for text, but lack of token-level annotated data hinders the effectiveness of supervised methods. |
| Approach: | They propose a Meta Teacher-Student (MetaTS) Network to alleviate data scarcity by leveraging large multilingual unlabeled data. |
| Outcome: | The proposed meta learning method alleviates data scarcity by leveraging large multilingual unlabeled data. |
Data-to-text Generation with Macro Planning (2021.tacl-1)
Copied to clipboard
| Challenge: | Recent approaches to data-to-text generation adopt the encoder-decoder architecture . however, these models perform poorly at selecting appropriate content and ordering it coherently . |
| Approach: | They propose a neural model with a macro planning stage followed by a generation stage . they use data from databases of records, simulations of physical systems, accounting spreadsheets . |
| Outcome: | The proposed model outperforms baselines on two data-to-text benchmarks . it uses the encoderdecoder architecture and is compared with existing models . |
Benchmarking Meta-embeddings: What Works and What Does Not (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to build meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. |
| Approach: | They propose a unified framework for a fair and objective meta-embedding evaluation using intrinsic and extrinsic tasks. |
| Outcome: | The proposed framework outperforms existing methods on intrinsic and extrinsic evaluation benchmarks and outperformed existing methods. |
Online Back-Parsing for AMR-to-Text Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Abstract meaning representation (AMR) is a semantic graph representation that abstracts meaning away from a sentence. |
| Approach: | They propose a decoder that back predicts projected AMR graphs on target sentences . their results show superiority over previous state-of-the-art decoded graph Transformer . |
| Outcome: | The proposed model outperforms the state-of-the-art model on two AMR benchmarks. |
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation (2022.findings-acl)
Copied to clipboard
| Challenge: | a new text generation dataset is needed to controllable text summarization, but it lacks the domain knowledge. |
| Approach: | They propose to use existing text generation datasets to leverage input and control signals . they propose to annotate each meta-review sentence manually with a control signal . |
| Outcome: | The proposed method can be used to control the structure of a text generation dataset . it can be applied to a variety of tasks, including a task with a large number of meta-review sentences . |
Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters (2023.eacl-main)
Copied to clipboard
| Challenge: | Recent approaches to text generation from Abstract Meaning Representation (AMR) have been based on neural-centered encoderdecoder architectures. |
| Approach: | They propose a structure-aware adapter which injects the input graph connectivity within PLMs using Graph Neural Networks. |
| Outcome: | The proposed adapter is robust to a variety of approaches and can be used to generate Graph-to-Text representations. |
Enabling Language Models to Fill in the Blanks (2020.acl-main)
Copied to clipboard
| Challenge: | Infilling is the task of predicting missing spans of text at any position in a document. |
| Approach: | They propose a framework which can be used to infill entire sentences . they train off-the-shelf LMs on sequences containing concatenation of masked text . |
| Outcome: | The proposed approach can infill entire sentences on short stories, scientific abstracts, and lyrics. |