Medical question summarization is an important but difficult task, where the input is often complex and erroneous while annotated data is expensive to acquire. We report our participation in the MEDIQA 2021 question summarization task in which we are required to address these challenges. We start from pre-trained conditional generative language models, use knowledge bases to help correct input errors, and rerank single system outputs to boost coverage. Experimental results show significant improvement in string-based metrics.

The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions. Ambiguous, misspelled, and incomplete entity mention names are the main challenges in the linking process. We propose a novel approach that combines two state-of-the-art models ― for entity disambiguation and for paraphrase detection ― to overcome these challenges. We consider name variations as paraphrases of the same entity mention and adopt a paraphrase model for this task. Our approach utilizes a graph-based disambiguation model based on Personalized Page Rank, and then refines and clusters its output using the paraphrase similarity between entity mention strings. It achieves a competitive performance of 80.5% in B3+F clustering score on diagnostic TAC EDL 2014 data.

We attempt to identify citations in non-academic text such as patents. Unlike academic articles which often provide bibliographies and follow consistent citation styles, non-academic text cites scientific research in a more ad-hoc manner. We manually annotate citations in 50 patents, train a CRF classifier to find new citations, and apply a reranker to incorporate non-local information. Our best system achieves 0.83 F-score on 5-fold cross validation.

Relations (ABBREVIATE, EXEMPLIFY, ORIGINATE, REL_WORK, OPINION) between entities (citations, jargon, people, organizations) are annotated for PubMed scientific articles. We discuss our specifications, pre-processing and evaluation

In this tutorial, we cover techniques that facilitate the integration of Machine Translation (MT) and Translation Memory (TM), which can help the adoption of MT technology in localisation industry. The tutorial covers four parts: i) brief introduction of MT and TM systems, ii) MT confidence estimation measures tailored for the TM environment, iii) segment-level MT and MT integration, iv) sub-segment level MT and TM integration, and v) human evaluation of MT and TM integration. We will first briefly describe and compare how translations are generated in MT and TM systems, and suggest possible avenues to combines these two systems. We will also cover current quality / cost estimation measures applied in MT and TM systems, such as the fuzzy-match score in the TM, and the evaluation/confidence metrics used to judge MT outputs. We then move on to introduce the recent developments in the field of MT confidence estimation tailored towards predicting post-editing efforts. We will especially focus on the confidence metrics proposed by Specia et al., which is shown to have high correlation with human preference, as well as post-editing time. For segment-level MT and TM integration, we present translation recommendation and translation re-ranking models, where the integration happens at the 1-best or the N-best level, respectively. Given an input to be translated, MT-TM recommendation compares the output from the MT and the TM systems, and presents the better one to the post-editor. MT-TM re-ranking, on the other hand, combines k-best lists from both systems, and generates a new list according to estimated post-editing effort. We observe high precision of these models in automatic and human evaluations, indicating that they can be integrated into TM environments without the risk of deteriorating the quality of the post-editing candidate. For sub-segment level MT and TM integration, we try to reuse high quality TM chunks to improve the quality of MT systems. We can also predict whether phrase pairs derived from fuzzy matches should be used to constrain the translation of an input segment. Using a series of linguistically- motivated features, our constraints lead both to more consistent translation output, and to improved translation quality, as is measured by automatic evaluation scores. Finally, we present several methodologies that can be used to track post-editing effort, perform human evaluation of MT-TM integration, or help translators to access MT outputs in a TM environment.

We report findings from a user study with professional post-editors using a translation recommendation framework (He et al., 2010) to integrate Statistical Machine Translation (SMT) output with Translation Memory (TM) systems. The framework recommends SMT outputs to a TM user when it predicts that SMT outputs are more suitable for post-editing than the hits provided by the TM. We analyze the effectiveness of the model as well as the reaction of potential users. Based on the performance statistics and the users’ comments, we find that translation recommendation can reduce the workload of professional post-editors and improve the acceptance of MT in the localization industry.