We propose encoder-centric stepwise models for extractive summarization using structured transformers – HiBERT and Extended Transformers. We enable stepwise summarization by injecting the previously generated summary into the structured transformer as an auxiliary sub-structure. Our models are not only efficient in modeling the structure of long inputs, but they also do not rely on task-specific redundancy-aware modeling, making them a general purpose extractive content planner for different tasks. When evaluated on CNN/DailyMail extractive summarization, stepwise models achieve state-of-the-art performance in terms of Rouge without any redundancy aware modeling or sentence filtering. This also holds true for Rotowire table-to-text generation, where our models surpass previously reported metrics for content selection, planning and ordering, highlighting the strength of stepwise modeling. Amongst the two structured transformers we test, stepwise Extended Transformers provides the best performance across both datasets and sets a new standard for these challenges.

Conversational agents offer users a natural-language interface to accomplish tasks, entertain themselves, or access information. Informational dialogue is particularly challenging in that the agent has to hold a conversation on an open topic, and to achieve a reasonable coverage it generally needs to digest and present unstructured information from textual sources. Making responses based on such sources sound natural and fit appropriately into the conversation context is a topic of ongoing research, one of the key issues of which is preventing the agent’s responses from sounding repetitive. Targeting this issue, we propose a new task, known as redundancy localization, which aims to pinpoint semantic overlap between text passages. To help address it systematically, we formalize the task, prepare a public dataset with fine-grained redundancy labels, and propose a model utilizing a weak training signal defined over the results of a passage-retrieval system on web texts. The proposed model demonstrates superior performance compared to a state-of-the-art entailment model and yields encouraging results when applied to a real-world dialogue.

Guided by multiple heuristics, a unified taxonomy of entities and categories is distilled from the Wikipedia category network. A comprehensive evaluation, based on the analysis of upward generalization paths, demonstrates that the taxonomy supports generalizations which are more than twice as accurate as the state of the art. The taxonomy is available at http://headstaxonomy.com.

We present an annotated resource consisting of open-domain translation requests, automatic translations and user-provided corrections collected from casual users of the translation portal http://reverso.net. The layers of annotation provide: 1) quality assessments for 830 correction suggestions for translations into English, at the segment level, and 2) 814 usefulness assessments for English-Spanish and English-French translation suggestions, a suggestion being useful if it contains at least local clues that can be used to improve translation quality. We also discuss the results of our preliminary experiments concerning 1) the development of an automatic filter to separate useful from non-useful feedback, and 2) the incorporation in the machine translation pipeline of bilingual phrases extracted from the suggestions. The annotated data, available for download from ftp://mi.eng.cam.ac.uk/data/faust/LW-UPC-Oct11-FAUST-feedback-annotation.tgz, is released under a Creative Commons license. To our best knowledge, this is the first resource of this kind that has ever been made publicly available.

We present a corpus consisting of 11,292 real-world English to Spanish automatic translations annotated with relative (ranking) and absolute (adequate/non-adequate) quality assessments. The translation requests, collected through the popular translation portal http://reverso.net, provide a most variated sample of real-world machine translation (MT) usage, from complete sentences to units of one or two words, from well-formed to hardly intelligible texts, from technical documents to colloquial and slang snippets. In this paper, we present 1) a preliminary annotation experiment that we carried out to select the most appropriate quality criterion to be used for these data, 2) a graph-based methodology inspired by Interactive Genetic Algorithms to reduce the annotation effort, and 3) the outcomes of the full-scale annotation experiment, which result in a valuable and original resource for the analysis and characterization of MT-output quality.

We present a detailed analysis of a graph-based annotation strategy that we employed to annotate a corpus of 11,292 real-world English to Spanish automatic translations with relative (ranking) and absolute (adequate/non-adequate) quality assessments. The proposed approach, inspired by previous work in Interactive Evolutionary Computation and Interactive Genetic Algorithms, results in a simpler and faster annotation process. We empirically compare the method against a traditional, explicit ranking approach, and show that the graph-based strategy: 1) is considerably faster, and 2) produces consistently more reliable annotations.

This work extends phrase-based statistical MT (SMT) with shallow syntax dependencies. Two string-to-chunks translation models are proposed: a factored model, which augments phrase-based SMT with layered dependencies, and a joint model, that extends the phrase translation table with microtags, i.e. per-word projections of chunk labels. Both rely on n-gram models of target sequences with different granularity: single words, micro-tags, chunks. In particular, n-grams defined over syntactic chunks should model syntactic constraints coping with word-group movements. Experimental analysis and evaluation conducted on two popular Chinese-English tasks suggest that the shallow-syntax joint-translation model has potential to outperform state-of-the-art phrase-based translation, with a reasonable computational overhead.