Papers with neural framework
Joint Learning from Labeled and Unlabeled Data for Information Retrieval (C18-1)
Copied to clipboard
| Challenge: | Recent studies have focused on neural information retrieval (IR) models. |
| Approach: | They propose a framework which can benefit from both labeled and more abundant unlabeled data . they propose supervised retrieval over several strong baselines for IR . |
| Outcome: | The proposed framework can benefit from labeled and more abundant unlabeled data for representation learning in the context of IR. |
Vocabulary Tailored Summary Generation (C18-1)
Copied to clipboard
| Challenge: | Existing frameworks for summary generation do not account for linguistic preferences of the specific audience who will consume the summary. |
| Approach: | They propose a neural framework to generate summary constrained to a vocabulary-defined linguistic preferences of a target audience. |
| Outcome: | The proposed approach generates understandable summaries with simpler words and readable summary with shorter words against a state-of-the-art word embedding based lexical substitution algorithm. |
Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in text summarization have overcome position bias in news articles . however, there are long-standing, unresolved challenges in extractive summarizing . |
| Approach: | They propose a neural framework that can flexibly control summary generation by introducing a set of sub-aspect functions. |
| Outcome: | The proposed framework can flexibly control summary generation by introducing sub-aspect functions . extracted summaries with minimal position bias are comparable with standard models . |
Weighted self Distillation for Chinese word segmentation (2022.findings-acl)
Copied to clipboard
| Challenge: | Recent researches show that multi-criteria resources and n-gram features are beneficial to Chinese word segmentation (CWS). |
| Approach: | They propose a framework that uses weighted self distillation to learn Chinese word segmentation using unigram features. |
| Outcome: | The proposed framework achieves state-of-the-art or competitive performance on SIGHAN Bakeoff datasets. |
HABLex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation (D19-1)
Copied to clipboard
| Challenge: | Existing methods to incorporate bilingual lexicons into statistical machine translation are unclear how to do so in the neural framework. |
| Approach: | They present a dataset to test methods for bilingual lexicon integration into neural machine translation using human generated alignments of words and phrases in three language pairs. |
| Outcome: | The proposed method improves on baselines and improves training to address overfitting. |
Curriculum Learning for Domain Adaptation in Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) performance drops when domains do not match and in-domain training data is scarce. |
| Approach: | They propose a curriculum learning approach to adapt generic neural machine translation models to a specific domain. |
| Outcome: | The proposed approach outperforms unadapted and adapted baselines in two domains and two language pairs. |
COMET: A Neural Framework for MT Evaluation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Historically, metrics for evaluating the quality of machine translation (MT) have relied on basic, lexical-level features such as counting the number of matching n-grams between the MT hypothesis and the reference translation. |
| Approach: | They propose a neural framework for training multilingual machine translation evaluation models which exploits human judgements to obtain new state-of-the-art levels of correlation with MT quality. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems. |
A Unified Neural Coherence Model (D19-1)
Copied to clipboard
| Challenge: | Existing models for coherence modeling fail on harder tasks with more realistic application scenarios. |
| Approach: | They propose a unified coherence model that incorporates sentence grammar, inter-sentence coherent relations, and global coherency patterns into a common neural framework. |
| Outcome: | The proposed model outperforms existing models on local and global discrimination tasks and outperformed existing models by a good margin. |
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for Chinese word segmentation have high performance on benchmarks but are limited by the small-scale annotated corpus. |
| Approach: | They propose a framework that incorporates a lexicon-based graph convolutional network into the Transformer encoder to improve Chinese word segmentation (CWS) Chinese word is an essential and pre-processing step for many downstream NLP tasks. |
| Outcome: | The proposed framework captures the information of candidate words and improves performance on benchmarks and datasets. |
Multidirectional Associative Optimization of Function-Specific Word Representations (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to learning word associations rely on distributional hypothesis and learn a single word vector space based on word co-occurrences in large text corpora. |
| Approach: | They propose to induce a joint function-specific word vector space where vectors of plausible SVO compositions lie close together. |
| Outcome: | The proposed model outperforms existing models on selectional preference and event similarity tasks while reducing the number of parameters by up to 95%. |
deepQuest: A Framework for Neural-based Quality Estimation (C18-1)
Copied to clipboard
| Challenge: | Predicting Machine Translation (MT) quality has been limited to word and sentence-level prediction. |
| Approach: | They propose a framework that can generalize neural QE approaches to the level of documents. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches on document-level quality estimates and is 40 times faster to train. |
Joint Effects of Context and User History for Predicting Online Conversation Re-entries (P19-1)
Copied to clipboard
| Challenge: | Existing methods for predicting online conversation re-entry focus on modeling engagement patterns in ongoing conversations or ignoring the rich information in users' previous chatting history. |
| Approach: | They propose a neural framework with three main layers to model the conversation context and user history and their interactions with Twitter and Reddit to predict whether a user will return to a conversation they once participated in. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two large-scale Twitter and Reddit conversations, and achieves an F1 score of 61.1 on Twitter conversations. |
MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types (2023.findings-acl)
Copied to clipboard
Keerthiram Murugesan, Sarathkrishna Swaminathan, Soham Dan, Subhajit Chaudhury, Chulaka Gunasekara, Maxwell Crouse, Diwakar Mahajan, Ibrahim Abdelaziz, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Alexander Gray
| Challenge: | Existing evaluation metrics for machine text are inadequate to capture quality of text . a recent study has focused on task-specific evaluation metrics or on properties of machine-generated text based on mismatch errors . |
| Approach: | They propose a new evaluation scheme based on fine-grained mismatch errors . they propose 13 mismatch error types to guide the model for better prediction of human judgments . |
| Outcome: | The proposed evaluation scheme is based on mismatch errors in 7 NLP tasks . the mismatch error types guide the model for better prediction of human judgments . |
Competing Independent Modules for Knowledge Integration and Optimization (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing knowledge sources are not available for sentiment analysis, but are used for many tasks. |
| Approach: | They propose a framework of untied independent modules for integrating off the shelf knowledge sources such as language models, lexica, POS information, and dependency relations. |
| Outcome: | The proposed framework is suitable for optimizing BERT-like language models even without external knowledge sources. |
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised (D18-1)
Copied to clipboard
| Challenge: | Existing methods for opinion summarization are knowledge-lean and require light supervision. |
| Approach: | They propose a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision. |
| Outcome: | The proposed framework improves over baselines and shows that opinion summaries are preferred by human judges according to multiple criteria. |
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing (P19-1)
Copied to clipboard
| Challenge: | a new neural framework for sentence-level discourse analysis is proposed . a discourse segmenter and a parser are based on pointer networks and operate in linear time . |
| Approach: | They propose a neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory . they use a discourse segmenter and a parser to construct a discursive tree in a top-down fashion . |
| Outcome: | The proposed framework surpasses previous approaches on both tasks and human agreement on both. |
Improving Chinese Word Segmentation with Wordhood Memory Networks (2020.acl-main)
Copied to clipboard
| 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. |