Papers with neural methods
Empirical Evaluation of Active Learning Techniques for Neural MT (D19-61)
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| Challenge: | Several active learning (AL) algorithms for machine translation (MT) have been well-studied for phrase-based MT. |
| Approach: | They propose to use a phrase-based algorithm to compare different AL methods in a simulated AL framework to demonstrate how unsupervised pre-training and paraphrastic embedding can be used to improve existing AL methods. |
| Outcome: | The proposed method outperforms existing methods in the context of phrase-based MT and is based on a simulated phrase-driven dataset. |
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension (2020.tacl-1)
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| Challenge: | ''Language is, at best, a means of directing others to construct similar-thoughts from their own prior knowledge,'' says K. S. Adams and Bruce. |
| Approach: | They present a free-form multiple-choice Chinese machine reading Comprehension dataset (C3) containing 13,369 documents and their associated 19,577 multiple-CHOice free- form questions. |
| Outcome: | The proposed model outperforms human models on linguistic, domain-specific, and general world knowledge problems. |
A Corpus and Method for Chinese Named Entity Recognition in Manufacturing (2024.lrec-main)
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| Challenge: | Existing resources and techniques for named entity recognition (NER) for manufacturing-specific named entities are limited. |
| Approach: | They propose a corpus of Chinese manufacturing specifications, named MS-NERC, with 4,424 sentences and 16,383 entities. |
| Outcome: | The proposed model outperforms neural methods in few-shot and rich-resource domains. |
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)
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Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat
| Challenge: | Existing methods for relation extraction (RE) use only expanded facts from the knowledge graph . |
| Approach: | They propose a method for relation extraction from a single sentence . they use a neural network to expand the context with additional facts from the KG . |
| Outcome: | The proposed method is more accurate than state-of-the-art methods on standard datasets. |
Neural Event Semantics for Grounded Language Understanding (2021.tacl-1)
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| Challenge: | a new framework for compositional grounded language understanding is proposed . NES is trainable end-to-end by gradient descent with minimal supervision. |
| Approach: | They propose a conjunctivist framework for compositional grounded language understanding . they use words as classifiers that compose to form a sentence meaning by multiplying output scores . |
| Outcome: | The proposed framework improves on compositional grounded language tasks. |
Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task (N18-1)
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| Challenge: | Previously, neural methods in grammatical error correction did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) systems that improve on results by SMT use their set-up as a backbone for more complex systems. |
| Approach: | They propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. |
| Outcome: | The proposed methods outperform state-of-the-art neural GEC systems by 10% M2 on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. |
Low-Resource Multilingual and Zero-Shot Multispeaker TTS (2022.aacl-main)
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| Challenge: | Currently, the amount of data needed for TTS is limited to the vast majority of the spoken languages. |
| Approach: | They propose to use language agnostic meta learning procedure to learn speaking a new language with just 5 minutes of training data while retaining the ability to infer the voice of even unseen speakers. |
| Outcome: | The proposed approach is able to learn speaking a new language using just 5 minutes of training data while retaining the ability to infer the voice of even unseen speakers in the newly learned language. |
Boundary Detection with BERT for Span-level Emotion Cause Analysis (2021.findings-acl)
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| Challenge: | Emotion cause analysis (ECA) is an emerging topic in natural language processing, which aims to identify the reasons behind a given emotion. |
| Approach: | They propose to detect the precise boundaries of text spans conveying accurate emotion causes from the given context by a sequence labeling and position identification problem. |
| Outcome: | The proposed methods outperform existing models on two benchmark datasets on the emotion cause analysis task. |
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters (N19-1)
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| Challenge: | Existing literature analysis does not focus on roles of characters or on relationships between them. |
| Approach: | They propose to combine emotion and character identification into a unified framework for character network extraction from fictional texts. |
| Outcome: | The proposed task is based on fan-fiction short stories and is able to predict emotion relations in the extracted network graph. |
Pushing the Limits of Low-Resource Morphological Inflection (D19-1)
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| Challenge: | Recent advances in morphological inflection generation have limited resources . antonisa and colleagues present a battery of improvements to improve performance under low-resource conditions . |
| Approach: | They propose a two-step attention architecture for the inflection decoder that uses two-segments attention and a multi-single-syllabic attention architecture. |
| Outcome: | The proposed model outperforms the state-of-the-art in low-resource languages by 15 percentage points . the proposed model also shows that it can be used to model monolingual data hallucinations . |
What’s Going On in Neural Constituency Parsers? An Analysis (N18-1)
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| Challenge: | a number of differences have emerged between classical and modern constituency parsing approaches . structural components like grammars and feature-rich lexicons are becoming less central . recurrent neural networks have gained traction as a powerful and general purpose tool for representation . |
| Approach: | They propose a model that implicitly learns to encode much of the same information as grammars and lexicons in the past. |
| Outcome: | The proposed model outperforms state-of-the-art models under similar conditions. |
Smaller Text Classifiers with Discriminative Cluster Embeddings (N18-2)
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| Challenge: | Word embeddings dominate overall model sizes in neural methods for natural language processing, especially when large vocabularies and high dimensions are used. |
| Approach: | They propose a Gumbel-Softmax distribution to maximize over the latent clustering while minimizing the task loss. |
| Outcome: | The proposed method minimizes the task loss while maximizing over the latent clustering while remaining parameter-efficient. |
MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)
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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou
| Challenge: | Existing datasets exhibit data scarcity and limited coverage of general-domain events. |
| Approach: | They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types. |
| Outcome: | The proposed dataset shows that existing methods cannot achieve promising results on the small datasets. |
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Recent studies on single-document summarization (SDS) benefit from advances in neural sequence learning, but they produce unsatisfactory results on multi-document summary (MDS). |
| Approach: | They propose a neural sequence learning method that unifies advanced neural SDS methods and statistical measures used in classical MDS. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmark MDS datasets. |
TEN: Table Explicitization, Neurosymbolically (2026.acl-industry)
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| Challenge: | Existing methods for extracting tabular data from semistructured text are error-prone and costly. |
| Approach: | They propose a neurosymbolic approach to extract tabular data from semistructured text . TEN is a triadic feedback loop that iteratively refines table hypotheses . |
| Outcome: | The proposed approach outperforms neural baselines in exact match accuracy and lower hallucination rates. |
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision (2021.emnlp-main)
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Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, Maosong Sun
| Challenge: | Recent state-of-the-art (SOTA) effective neural network methods have been used in Chinese word segmentation (CWS) However, the robustness of the previous neural methods is limited by the large-scale annotated corpus. |
| Approach: | They propose a self-supervised Chinese word segmentation approach with a straightforward and effective architecture. |
| Outcome: | The proposed approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness. |
Studying the Evolution of Scientific Topics and their Relationships (2021.findings-acl)
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| Challenge: | a study of scientific topics and their evolution through time is proposed . we analyze scientific texts published in the field of computational linguistics . |
| Approach: | They propose a multidimensional approach to studying scientific topics through time and their relationships between them. |
| Outcome: | The proposed model analyzes scientific texts published in the ACL Anthology and compares them with case studies to understand how topics evolve and disappear over time. |
An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search (N19-1)
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| Challenge: | Neural encoder-decoder models have been successful at a variety of NLP tasks, including machine translation, parsing, and dialog generation. |
| Approach: | They propose a method for search-aware training via a continuous relaxation of beam search to enable global normalization. |
| Outcome: | The proposed approach is able to train globally normalized recurrent sequence models through simple backpropagation. |
Automatically Identifying Gender Issues in Machine Translation using Perturbations (2020.findings-emnlp)
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| Challenge: | a novel approach to machine translation has addressed outstanding challenges, including the modeling and treatment of gendered language. |
| Approach: | They propose a method to mine examples from real world data to explore challenges for deployed systems. |
| Outcome: | The proposed method exposes where model representations are gendered and the unintended consequences of genderes in downstream applications. |
Adaptive Mixed Component LDA for Low Resource Topic Modeling (2021.eacl-main)
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| Challenge: | Probabilistic topic models in low resource settings are faced with less reliable estimates due to sparsity of discrete word co-occurrence counts. |
| Approach: | They propose a mixture model which interpolates between discrete and continuous topic-word distributions and utilises pre-trained embeddings to improve topic coherence. |
| Outcome: | The proposed model outperforms fully discrete, fully continuous, and static mixture models on topic coherence in low resource settings. |
Pedagogically Aligned Objectives Create Reliable Automatic Cloze Tests (2024.naacl-long)
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| Challenge: | a recent study has focused on the cloze training objective of Masked Language Models . distractors must be distinct and incorrect, and can be biased if the test creator is testing two versions of a text . |
| Approach: | They propose a method that jointly optimizes sets of distractors from Masked Language Models. |
| Outcome: | The proposed method has stronger correlation with teacher-created comprehension tests than state-of-the-art neural method and is more internally consistent. |
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation (2021.findings-emnlp)
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| 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. |
ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language (2021.findings-acl)
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| Challenge: | Recent work shows that transformers can generate both implications of a theory and the natural language proofs that support them. |
| Approach: | They propose a generative model that generates both implications of a theory and natural language proofs that support them. |
| Outcome: | The proposed model generates both implications of a theory and the natural language proofs that support them. |
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model (D19-1)
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| Challenge: | Existing methods for data-to-text generation are insufficient to produce long and diverse texts. |
| Approach: | They propose a planning-based hierarchical variational model that plans a sequence of groups and then realizes each sentence conditioned on the planning result and the previously generated context. |
| Outcome: | The proposed model outperforms state-of-the-art models in long and diverse text generation. |
How low is too low? A monolingual take on lemmatisation in Indian languages (2021.naacl-main)
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| Challenge: | Prior work on ML based lemmatization focused on high resource languages, where data sets (word forms) are readily available. |
| Approach: | They propose to use neural methods to relate inflected forms of words to their dictionary form to reduce the sparse data problem. |
| Outcome: | The proposed methods can give competitive accuracy even in low resource setting. |
A Data-driven Approach to Named Entity Recognition for Early Modern French (2022.coling-1)
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| Challenge: | Named entity recognition is an important task in natural language processing. |
| Approach: | They propose to use a data-driven approach to identify historical French with fine-grained annotations instead of a specialised architecture to tackle particularities. |
| Outcome: | The proposed corpus is larger than the most popular NER evaluation corpora for both Contemporary English and French. |
Paraphrase Generation: A Survey of the State of the Art (2021.emnlp-main)
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| Challenge: | Using neural models, paraphrase generation research has shifted to neural methods . a recent study focused on paraphrases, which are used in language understanding tasks . |
| Approach: | They propose to use neural methods to generate fluent, diverse paraphrases from a sentence . they propose to combine large pretrained language models with other mechanisms to generate more advanced paraphrase generation models. |
| Outcome: | This paper examines various approaches to paraphrase generation with a main focus on neural methods. |
Explaining Dialogue Evaluation Metrics using Adversarial Behavioral Analysis (2022.naacl-main)
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| Challenge: | Existing frameworks for dialogue model evaluation are lacking to investigate these biases . a number of dialogue metrics are biased and can cause unforeseen problems . |
| Approach: | They propose an adversarial test-suite which generates problematic variations of various dialogue aspects using automatic heuristics. |
| Outcome: | The proposed test-suite generates problematic variations of various dialogue aspects using automatic heuristics. |
PEDANTS: Cheap but Effective and Interpretable Answer Equivalence (2024.findings-emnlp)
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| Challenge: | Current short-form QA evaluations lack diverse styles of evaluation data and rely on expensive and slow LLMs. |
| Approach: | They propose a rubric for machine QA that is more stable than an exact match and neural methods. |
| Outcome: | The proposed evaluations improve on the existing short-form QA evaluations using the Trivia community. |
Automatic Argument Quality Assessment - New Datasets and Methods (D19-1)
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Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | 6.3k arguments were collected from contributors of various levels, and are released as part of this work. |
| Approach: | They propose to use a language model to annotate arguments for argument ranking and argument-pair classification. |
| Outcome: | The proposed methods outperform state-of-the-art methods in the argument ranking task and argument-pair classification task. |
Authorship Attribution for Neural Text Generation (2020.emnlp-main)
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| Challenge: | Recent advances in deep learning have enabled the generation of realistic artifacts . however, the qualities of texts generated by these models are better, often confusing classifiers if they are not real. |
| Approach: | They propose to use neural network-based language models to generate realistic texts . they investigate the authorship attribution problem in three versions of a text . |
| Outcome: | The proposed models generate texts that are difficult to distinguish from human-written ones . the results show that most generators still generate texts significantly different from human ones compared to other models . |