Morphological Inflection Generation Using Character Sequence to Sequence Learning

By Manaal Faruqui, Yulia Tsvetkov, Graham Neubig, Chris Dyer. 2015

141 citations recorded by the source corpus.

Morphological inflection generation is the task of generating the inflected form of a given lemma corresponding to a particular linguistic transformation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervised and semi-supervised settings. We evaluate our system on seven datasets of morphologically rich languages and achieve either better or comparable results to existing state-of-the-art models of inflection generation.

Subfields: Artificial Intelligence, Natural Language Processing, Computational Linguistics & Semantics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Graham Neubig
2. Yonatan Belinkov
3. Yoav Goldberg
4. Mohit Bansal
5. Noah A. Smith
6. Luke S. Zettlemoyer
7. Hinrich Schütze
8. Yejin Choi

In Natural Language Processing

1. Graham Neubig
2. Yoav Goldberg
3. Chris Dyer
4. Noah A. Smith
5. Yonatan Belinkov
6. Hinrich Schütze
7. Mohit Bansal
8. Yejin Choi

In Computational Linguistics & Semantics

1. Yoav Goldberg
2. Alessandro Raganato
3. Hinrich Schütze
4. Roee Aharoni
5. Marine Carpuat
6. Katharina Kann
7. Weijia Xu
8. Peng Qian

Citations in the corpus

• Investigating Language Universal and Specific Properties in Word Embeddings (2016, background): “Despite its popularity, character-based approach also comes out as an equal competitor (Santos and Zadrozny, 2014; Kim et al., 2016; Ling et al., 2015b; Ling et al., 2015a; Faruqui et al., 2016; Ballesteros et al., 2015) .”
• Neural Sequence Learning Models for Word Sense Disambiguation (2017, methodology): “In particular, sequence-to-sequence models (Sutskever et al., 2014) have grown increasingly popular and are used extensively in, e.g., Machine Translation (Cho et al., 2014; Bahdanau et al., 2015), Sentence Representation (Kiros et al., 2015), Syntactic Parsing (Vinyals et al., 2015), Conversation M”
• Rule-based Morphological Inflection Improves Neural Terminology Translation (2021, methodology): “The task was traditionally tackled using hand-engineered finite state transducer that relies on linguistic knowledge (Koskenniemi, 1984; Kaplan and Kay, 1994), while recent work has shown impressive results by modeling it using neural sequence-to-sequence models (Faruqui et al., 2016).”
• Gender-Aware Reinflection using Linguistically Enhanced Neural Models (2020, unknown): “Sequence-to-sequence models have achieved significant results in grammatical error correction (GEC) (Chollampatt and Ng, 2018; Junczys-Dowmunt et al., 2018; Grundkiewicz et al., 2019) and morphological reinflection tasks (Faruqui et al., 2016; Kann and Schütze, 2016; Aharoni and Goldberg, 2017).”
• Single-Model Encoder-Decoder with Explicit Morphological Representation for Reinflection (2016, methodology): “MED bears some resemblance to Faruqui et al. (2015)’s work.”
• DyNet: The Dynamic Neural Network Toolkit (2017, methodology): “Morphology: DyNet has been used for morphological inflection generation [21, 2] ( https://github.com/mfaruqui/morph-trans https://github.com/roeeaharoni/morphological-reinflection ).”
• Morphological Inflection Generation with Hard Monotonic Attention (2016, methodology): “This shows the robustness of our model also with large amounts of training examples, and the advantage the hard attention mechanism provides over the encoder-decoder approach of Faruqui et al. (2016) which does not employ an attention mechanism.”