Leveraging online user feedback to improve statistical machine translation

In this article we present a three-step methodology for dynamically improving a statistical machine translation (SMT) system by incorporating human feedback in the form of free edits on the system translations. We target at feedback provided by casual users, which is typically error-prone. Thus, we first propose a filtering step to automatically identify the better user-edited translations and discard the useless ones. A second step produces a pivot-based alignment between source and user-edited sentences, focusing on the errors made by the system. Finally, a third step produces a new translation model and combines it linearly with the one from the original system. We perform a thorough evaluation on a real-world dataset collected from the Reverso.net translation service and show that every step in our methodology contributes significantly to improve a general purpose SMT system. Interestingly, the quality improvement is not only due to the increase of lexical coverage, but to a better lexical selection, reordering, and morphology. Finally, we show the robustness of the methodology by applying it to a different scenario, in which the new examples come from an automatically Web-crawled parallel corpus. Using exactly the same architecture and models provides again a significant improvement of the translation quality of a general purpose baseline SMT system

Preliminary safety and efficacy of first-line pertuzumab combined with trastuzumab and taxane therapy for HER2-positive locally recurrent or metastatic breast cancer (PERUSE).

BACKGROUND: Pertuzumab combined with trastuzumab and docetaxel is the standard first-line therapy for HER2-positive metastatic breast cancer, based on results from the phase III CLEOPATRA trial. PERUSE was designed to assess the safety and efficacy of investigator-selected taxane with pertuzumab and trastuzumab in this setting. PATIENTS AND METHODS: In the ongoing multicentre single-arm phase IIIb PERUSE study, patients with inoperable HER2-positive advanced breast cancer (locally recurrent/metastatic) (LR/MBC) and no prior systemic therapy for LR/MBC (except endocrine therapy) received docetaxel, paclitaxel or nab-paclitaxel with trastuzumab [8\u2009mg/kg loading dose, then 6\u2009mg/kg every 3\u2009weeks (q3w)] and pertuzumab (840\u2009mg loading dose, then 420\u2009mg q3w) until disease progression or unacceptable toxicity. The primary end point was safety. Secondary end points included overall response rate (ORR) and progression-free survival (PFS). RESULTS: Overall, 1436 patients received at least one treatment dose (initially docetaxel in 775 patients, paclitaxel in 589, nab-paclitaxel in 65; 7 discontinued before starting taxane). Median age was 54\u2009years; 29% had received prior trastuzumab. Median treatment duration was 16\u2009months for pertuzumab and trastuzumab and 4\u2009months for taxane. Compared with docetaxel-containing therapy, paclitaxel-containing therapy was associated with more neuropathy (all-grade peripheral neuropathy 31% versus 16%) but less febrile neutropenia (1% versus 11%) and mucositis (14% versus 25%). At this preliminary analysis (52 months' median follow-up), median PFS was 20.6 [95% confidence interval (CI) 18.9-22.7] months overall (19.6, 23.0 and 18.1\u2009months with docetaxel, paclitaxel and nab-paclitaxel, respectively). ORR was 80% (95% CI 78%-82%) overall (docetaxel 79%, paclitaxel 83%, nab-paclitaxel 77%). CONCLUSIONS: Preliminary findings from PERUSE suggest that the safety and efficacy of first-line pertuzumab, trastuzumab and taxane for HER2-positive LR/MBC are consistent with results from CLEOPATRA. Paclitaxel appears to be a valid alternative taxane backbone to docetaxel, offering similar PFS and ORR with a predictable safety profile. CLINICALTRIALS.GOV: NCT01572038