6 research outputs found
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing
We introduce a novel method for multilingual transfer that utilizes deep
contextual embeddings, pretrained in an unsupervised fashion. While contextual
embeddings have been shown to yield richer representations of meaning compared
to their static counterparts, aligning them poses a challenge due to their
dynamic nature. To this end, we construct context-independent variants of the
original monolingual spaces and utilize their mapping to derive an alignment
for the context-dependent spaces. This mapping readily supports processing of a
target language, improving transfer by context-aware embeddings. Our
experimental results demonstrate the effectiveness of this approach for
zero-shot and few-shot learning of dependency parsing. Specifically, our method
consistently outperforms the previous state-of-the-art on 6 tested languages,
yielding an improvement of 6.8 LAS points on average.Comment: NAACL 201
Low-Resource Response Generation with Template Prior
We study open domain response generation with limited message-response pairs.
The problem exists in real-world applications but is less explored by the
existing work. Since the paired data now is no longer enough to train a neural
generation model, we consider leveraging the large scale of unpaired data that
are much easier to obtain, and propose response generation with both paired and
unpaired data. The generation model is defined by an encoder-decoder
architecture with templates as prior, where the templates are estimated from
the unpaired data as a neural hidden semi-markov model. By this means, response
generation learned from the small paired data can be aided by the semantic and
syntactic knowledge in the large unpaired data. To balance the effect of the
prior and the input message to response generation, we propose learning the
whole generation model with an adversarial approach. Empirical studies on
question response generation and sentiment response generation indicate that
when only a few pairs are available, our model can significantly outperform
several state-of-the-art response generation models in terms of both automatic
and human evaluation.Comment: Accepted by EMNLP201
Exploiting Cross-Lingual Representations For Natural Language Processing
Traditional approaches to supervised learning require a generous amount of labeled data for good generalization. While such annotation-heavy approaches have proven useful for some Natural Language Processing (NLP) tasks in high-resource languages (like English), they are unlikely to scale to languages where collecting labeled data is di cult and time-consuming. Translating supervision available in English is also not a viable solution, because developing a good machine translation system requires expensive to annotate resources which are not available for most languages.
In this thesis, I argue that cross-lingual representations are an effective means of extending NLP tools to languages beyond English without resorting to generous amounts of annotated data or expensive machine translation. These representations can be learned in an inexpensive manner, often from signals completely unrelated to the task of interest. I begin with a review of different ways of inducing such representations using a variety of cross-lingual signals and study algorithmic approaches of using them in a diverse set of downstream tasks. Examples of such tasks covered in this thesis include learning representations to transfer a trained model across languages for document classification, assist in monolingual lexical semantics like word sense induction, identify asymmetric lexical relationships like hypernymy between words in different languages, or combining supervision across languages through a shared feature space for cross-lingual entity linking. In all these applications, the representations make information expressed in other languages available in English, while requiring minimal additional supervision in the language of interest