| Challenge: | Recent studies have shown that neural transition-based models can be used for morphological tasks such as inflection generation and lemmatization without a character aligner or warm start. |
| Approach: | They propose to use imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization. |
| Outcome: | The proposed model eliminates the need for a character aligner or warm start and achieves state-of-the-art performance on several datasets. |
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Neural Transition-based String Transduction for Limited-Resource Setting in Morphology (C18-1)
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| Challenge: | Morphological string transduction involves mapping one word form into another, possibly given a feature specification for the mapping. |
| Approach: | They propose a neural transition-based model that uses a simple set of edit actions for morphological transduction tasks such as reinflection and reinflation. |
| Outcome: | The proposed model outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. |
Exact Hard Monotonic Attention for Character-Level Transduction (P19-1)
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| Challenge: | Neural sequence-to-sequence models with soft attention outperform monotonic models . current dominant method is the neural sequenceto-Sequency model with soft focus . |
| Approach: | They develop a hard attention sequence-to-sequence model that enforces strict monotonicity and learns alignment jointly. |
| Outcome: | The proposed model achieves state-of-the-art on grapheme-to-phoneme conversion and morphological inflection generation. |
Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models (2022.acl-short)
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| Challenge: | morphological inflection models have been successful with shared tasks . but they fail at generalizing inflation patterns when trained on a limited number of lemmata . |
| Approach: | They find that standard models fail at generalizing inflection patterns when trained on a limited number of lemmata and asked to inflect previously unseen lemma. |
| Outcome: | The proposed model can perform well on morphological inflection tasks if training data covers a diversity of lemmata or some variant of the input lemma has been witnessed during training. |
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning (2021.eacl-main)
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| Challenge: | Existing approaches to learn simultaneous translation model with coupled programmer-interpreter policies are suboptimal as they fix the agent's policy to focus learning the NMT model or learn adaptive agent policies while the NRT model is fixed. |
| Approach: | They propose an algorithmic oracle to produce oracular READ/WRITE actions for training bilingual sentence-pairs using the notion of word alignments. |
| Outcome: | The proposed method outperforms baselines in terms of translation quality quality while keeping the delay low. |
Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources (2021.eacl-main)
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| Challenge: | a hybrid approach to lemmatization enhances the seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. |
| Approach: | They propose a hybrid approach that enhances a seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. |
| Outcome: | The proposed model achieves statistically significant improvements on 23 UD languages, compared to baseline models not utilizing additional lemma information. |
What do character-level models learn about morphology? The case of dependency parsing (D18-1)
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| Challenge: | morphologically rich languages require character-level input models to learn morphology, but some models are poor at disambiguating some words . authors of this study show that character- level models learn a lot from input input . explicit modeling of morphologies is expensive and expensive, authors say . |
| Approach: | They compare character-level models to an oracle with explicit morphological analysis . they show that explicitly modeling morphology improves their best model . |
| Outcome: | The results show that character-level models learn morphology better than word models . the authors compare character-based models to oracles on 12 languages with morphological typologies . |
Self-Paced Learning for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing studies have shown that the training of neural machine translation (NMT) rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word rarity. |
| Approach: | They propose to train NMT model using a self-paced learning approach that allows it to quantify the learning confidence over training examples and flexibly govern its learning via regulating the loss in each iteration step. |
| Outcome: | The proposed model outperforms baseline models and those trained with human-designed curricula on translation quality and convergence speed. |
Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting (D18-1)
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| Challenge: | Existing lexicons have limited coverage for learning morphological inflection patterns from labeled data. |
| Approach: | They propose two new methods to solve paradigm completion, the morphological task of generating missing forms, given a partial paradigm. |
| Outcome: | The proposed methods outperform the previous state-of-the-art by 9.71% absolute accuracy on a 52-language benchmark dataset. |
Inducing and Using Alignments for Transition-based AMR Parsing (2022.naacl-main)
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Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramón Astudillo
| Challenge: | Abstract Meaning Representation parsers rely on node-to-word alignments, but lack the complexity of the pipeline. |
| Approach: | They propose a neural aligner for abstract meaning representation that learns node-to-word alignments without relying on pipelines. |
| Outcome: | The proposed approach improves accuracy and generalization from AMR2.0 to AMR3.0 corpora. |
Demonstration of a Neural Machine Translation System with Online Learning for Translators (P19-3)
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Miguel Domingo, Mercedes García-Martínez, Amando Estela Pastor, Laurent Bié, Alexander Helle, Álvaro Peris, Francisco Casacuberta, Manuel Herranz Pérez
| Challenge: | a new method of "humanizing" automatic translations has been developed for the translation industry . a demonstration of an online learning system for machine translation in a production environment . |
| Approach: | They present a system which implements online learning for neural machine translation in a production environment. |
| Outcome: | The proposed system saves post-editing effort and adapts to a specific domain or user style. |