In this paper, we model aspects of communication beyond the words that are said. Specifically, we aim to detect interruptions and active listening events, which are important elements in any dialogue. We build a dataset with fine-grained annotations for each category and train multimodal models that take into account all channels in a digital conversation, that is, the video, the audio, and the text. Our experiments show that multimodality is a necessary component in modeling the complexity of the non-textual components of the conversation as different artifacts require different modalities to capture effectively.

We show that Bayes’ rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents a compelling benefit because parallel documents are not always available. In our formulation, the posterior probability of a candidate translation is the product of the unconditional (prior) probability of the candidate output document and the “reverse translation probability” of translating the candidate output back into the source language. Our proposed model uses a powerful autoregressive language model as the prior on target language documents, but it assumes that each sentence is translated independently from the target to the source language. Crucially, at test time, when a source document is observed, the document language model prior induces dependencies between the translations of the source sentences in the posterior. The model’s independence assumption not only enables efficient use of available data, but it additionally admits a practical left-to-right beam-search algorithm for carrying out inference. Experiments show that our model benefits from using cross-sentence context in the language model, and it outperforms existing document translation approaches.

Solving algebraic word problems requires executing a series of arithmetic operations-a program-to obtain a final answer.However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge.To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps.Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones.To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales.Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.

We propose a general class of language models that treat reference as discrete stochastic latent variables.This decision allows for the creation of entity mentions by accessing external databases of referents (required by, e.g., dialogue generation) or past internal state (required to explicitly model coreferentiality).Beyond simple copying, our coreference model can additionally refer to a referent using varied mention forms (e.g., a reference to "Jane" can be realized as "she"), a characteristic feature of reference in natural languages.Experiments on three representative applications show our model variants outperform models based on deterministic attention and standard language modeling baselines.

Wang Ling, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Fumin Wang, Andrew Senior. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features.The curricula are modeled by a linear ranking function which is the scalar product of a learned weight vector and an engineered feature vector that characterizes the different aspects of the complexity of each instance in the training corpus.We show that learning the curriculum improves performance on a variety of downstream tasks over random orders and in comparison to the natural corpus order.

We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing.The unsupervised component is based on a generative model in which latent sentences generate the unpaired logical forms.We apply this method to a number of semantic parsing tasks focusing on domains with limited access to labelled training data and extend those datasets with synthetically generated logical forms.

We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model that is able to absorb information from multiple text units to construct informative, concise, and fluent summaries. An importance-based sampling method is designed to allow the encoder to integrate information from an important subset of input. Automatic evaluation indicates that our system outperforms state-of-the-art abstractive and extractive summarization systems on two newly collected datasets of movie reviews and arguments. Our system summaries are also rated as more informative and grammatical in human evaluation.

Microblogs such as Twitter, Facebook, and Sina Weibo (China's equivalent of Twitter) are a remarkable linguistic resource. In contrast to content from edited genres such as newswire, microblogs contain discussions of virtually every topic by numerous individuals in different languages and dialects and in different styles. In this work, we show that some microblog users post “self-translated” messages targeting audiences who speak different languages, either by writing the same message in multiple languages or by retweeting translations of their original posts in a second language. We introduce a method for finding and extracting this naturally occurring parallel data. Identifying the parallel content requires solving an alignment problem, and we give an optimally efficient dynamic programming algorithm for this. Using our method, we extract nearly 3M Chinese–English parallel segments from Sina Weibo using a targeted crawl of Weibo users who post in multiple languages. Additionally, from a random sample of Twitter, we obtain substantial amounts of parallel data in multiple language pairs. Evaluation is performed by assessing the accuracy of our extraction approach relative to a manual annotation as well as in terms of utility as training data for a Chinese–English machine translation system. Relative to traditional parallel data resources, the automatically extracted parallel data yield substantial translation quality improvements in translating microblog text and modest improvements in translating edited news content.

Ramon F. Astudillo, Silvio Amir, Wang Ling, Mário Silva, Isabel Trancoso. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, Noah A. Smith. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Wang Ling, Chris Dyer, Alan W Black, Isabel Trancoso, Ramón Fermandez, Silvio Amir, Luís Marujo, Tiago Luís. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015.

Wang Ling, Yulia Tsvetkov, Silvio Amir, Ramón Fermandez, Chris Dyer, Alan W Black, Isabel Trancoso, Chu-Cheng Lin. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015.

Unsupervisedly learned word vectors have proven to provide exceptionally effective features in many NLP tasks.Most common intrinsic evaluations of vector quality measure correlation with similarity judgments.However, these often correlate poorly with how well the learned representations perform as features in downstream evaluation tasks.We present QVEC-a computationally inexpensive intrinsic evaluation measure of the quality of word embeddings based on alignment to a matrix of features extracted from manually crafted lexical resources-that obtains strong correlation with performance of the vectors in a battery of downstream semantic evaluation tasks. 1

Wang Ling, Chris Dyer, Alan W. Black, Isabel Trancoso. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

Compared to the edited genres that have played a central role in NLP research, microblog texts use a more informal register with nonstandard lexical items, abbreviations, and free orthographic variation.When confronted with such input, conventional text analysis tools often perform poorly.Normalization -replacing orthographically or lexically idiosyncratic forms with more standard variants -can improve performance.We propose a method for learning normalization rules from machine translations of a parallel corpus of microblog messages.To validate the utility of our approach, we evaluate extrinsically, showing that normalizing English tweets and then translating improves translation quality (compared to translating unnormalized text) using three standard web translation services as well as a phrase-based translation system trained on parallel microblog data.