Source-linked AI summary
Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders
Antonio Valerio Miceli Barone
TL;DR
Existing cross-lingual representation approaches require parallel resources, and this paper asks whether monolingual corpora can suffice. It uses an adversarial autoencoder to map source word vectors toward a target embedding distribution, reporting some semantic transfer but results that are not competitive with other approaches.
Problem
Existing approaches to compatible cross-lingual representations require parallel text or other parallel resources.
Source: §1 · p. 1Method
Results
The mapping appears to transfer some semantic information, but it is not competitive with other cross-lingual representation approaches.
Source: §4 · p. 4Takeaways & Limitations
The work initiates research into using distribution matching, including adversarial training, to learn language isomorphisms.
Source: §4 · p. 5Further experimentation is needed to determine whether the method can improve or whether monolingual distribution matching has a fundamental limit on semantic transfer.
Source: §4 · p. 4
Abstract
from arXiv · showhide
Current approaches to learning vector representations of text that are compatible between different languages usually require some amount of parallel text, aligned at word, sentence or at least document level. We hypothesize however, that different natural languages share enough semantic structure that it should be possible, in principle, to learn compatible vector representations just by analyzing the monolingual distribution of words. In order to evaluate this hypothesis, we propose a scheme to map word vectors trained on a source language to vectors semantically compatible with word vectors trained on a target language using an adversarial autoencoder. We present preliminary qualitative results and discuss possible future developments of this technique, such as applications to cross-lingual sentence representations.
1 Introduction
Earlier cross-lingual representation methods rely on parallel resources, while this work explores whether monolingual corpora with broadly similar themes can support compatible representations. It hypothesizes that languages’ semantic structures are sufficiently similar to learn a mapping from monolingual statistics.
1 Introduction
- Distributed representations map text units to vectors and provide machine-learning models with language knowledge learned from unannotated corpora (Bengio et al., 2006; Collobert and Weston, 2008; Turian et al., 2010; Maas et al., 2011; Mikolov et al., 2013b; Socher et al., 2013; Pennington et al., 2014; Levy and Goldberg, 2014; Le and Mikolov, 2014; Baroni et al., 2014; Levy et al., 2015).Source: §1 · p. 1
- Earlier approaches to compatible cross-lingual representations require parallel text or other parallel resources.These resources may be aligned at the word, sentence, or document level.Source: §1 · p. 1
- The paper explores whether thematically similar monolingual corpora can yield compatible representations without parallel resources.The proposed investigation relies on thematic similarity between languages only in a general sense.Source: §1 · p. 1
- The authors hypothesize that languages conveying similar information in similar contexts have approximately isomorphic vector-generating processes learnable from monolingual corpus statistics.The hypothesis allows for representations of words, sentences, paragraphs, or documents, without explicit alignment.Source: §1 · p. 1
- Multilingual speakers’ ability to translate with little explicitly aligned input motivates the possibility of shared or readily transformable language representations.The passage suggests speakers may have shared conceptual representations or distinct but near-isomorphic ones.Source: §1 · pp. 1–2
2 Learning word embedding cross-lingual mappings with adversarial autoencoders
The section first considers adversarial distribution matching for mapping source word embeddings to a target distribution, then turns to an adversarial autoencoder after the generator collapses to near-constant outputs. The autoencoder combines adversarial matching with reconstruction and a further penalty on generated and target samples.
2 Learning word embedding cross-lingual mappings with adversarial autoencoders
- In the initial GAN approach, a discriminator distinguishes target embeddings from generated source embeddings, while the generator is trained to fool it.The paper considers GANs rather than GMMNs because GAN effectiveness had been demonstrated more thoroughly in the literature (Li et al., 2015; Goodfellow et al., 2014).Source: §2 · p. 2, Figure 1 · p. 2
- The GAN setup quickly produced constant or near-constant generator outputs despite changes to network architectures and hyperparameters.The authors describe this as an extreme mode-seeking outcome, possibly exacerbated by word embeddings’ point-mass distribution.Source: §2 · p. 3
- The adversarial autoencoder pairs an encoder that maps source embeddings toward the target distribution with a decoder that reconstructs its inputs.The discriminator matches the encoder’s latent representation to the target embedding distribution, rather than imposing a known prior.Source: §2 · p. 3, Figure 2 · p. 3
- To prevent the generator from discarding input information, the autoencoder adds a decoder and jointly minimizes reconstruction and adversarial losses.The discriminator continues to match the latent representation to the target embedding distribution.Source: §2 · p. 3
- The encoder-decoder objective combines reconstruction loss, adversarial loss, and pairwise cosine dissimilarity between generated latent samples and true target samples.The experiments set all three loss weights to 1.Source: §2 · p. 3
3 Experiments
The experiments test English-to-Italian semantic transfer qualitatively and German-English transformations on document classification, using preliminary evaluations with different setups.
3 Experiments
- The qualitative experiment used word2vec skipgram embeddings of dimension d = 100, trained on about 1.5 million Wikipedia sentences per language, with linear encoders and decoders sharing tied matrices.The experiment mapped English embeddings to Italian embeddings.Source: §3 · p. 3
- The discriminator used residual blocks with batch normalization and leaky ReLU, and the authors report that this architecture yielded larger gradients to the generator than a standard multilayer perceptron.They trained one discriminator for the generator and a second, separately initialized discriminator for monitoring.Source: §3 · pp. 3–4
- English-to-Italian nearest neighbors sometimes included the true translation or semantically related terms, while place names such as “France” mapped incorrectly.Examples included “computer” → “computer” and “rain” → Italian terms associated with cold or wet weather.Source: §3 · p. 4
- The German-English model improved over baseline only at the smallest training-set size, using Reuters and News Commentary 2015 embeddings with d = 40 and discriminator depth T = 4.The evaluation followed the setup of Klementiev et al. (2012).Source: §3 · p. 4
4 Discussion and future work
The mappings appear to transfer some semantic information but are not competitive with other cross-lingual approaches. Further experiments are needed to assess whether monolingual distribution matching can improve and to test possible uses alongside parallel training.
4 Discussion and future work
- The mappings appear to transfer some semantic information, but are not competitive with other cross-lingual representation approaches.The authors suggest hyperparameter choices and architectural details may explain the gap.Source: §4 · p. 4
- Further experimentation is needed to determine whether the model can improve or whether monolingual distribution matching has a fundamental limit on semantic transfer.The authors also identify testing the semantic-isomorphism hypothesis across different linguistic families as a target for further work.Source: §4 · p. 4
- The technique might help alongside parallel-data training even if semantic transfer from monolingual text alone proves infeasible.Proposed applications include adversarial alignment of latent representations in sequence-to-sequence machine translation.Source: §4 · p. 4
- The authors propose evaluating GMMs, BiGAN/ALI, and newer GAN training techniques as additional distribution-learning approaches.BiGAN/ALI uses an adversarial discriminator loss both to match latent distributions and to enforce reconstruction.Source: §4 · pp. 4–5
- The work presents adversarial distribution matching as a potentially promising research direction for learning isomorphisms between languages.Source: §4 · p. 5