The use of stopwords has been thoroughly studied in traditional Information Retrieval systems, but remains unexplored in the context of neural models. Neural re-ranking models take the full text of both the query and document into account. Naturally, removing tokens that do not carry relevance information provides us with an opportunity to improve the effectiveness by reducing noise and lower document representation caching-storage requirements. In this work we propose a novel contextualized stopword detection mechanism for neural re-ranking models. This mechanism consists of training a sparse vector in order to filter out document tokens from the ranking decision. This vector is learned end-to-end based on the contextualized document representations, allowing the model to filter terms on a per occurrence basis. This leads to a more explainable model, as it reduces noise. We integrate our component into the state-of-the-art interaction-based TK neural re-ranking model. Our experiments on the MS MARCO passage collection and queries from the TREC 2019 Deep Learning Track show that filtering out traditional stopwords prior to the neural model reduces its effectiveness, while learning to filter out contextualized representations improves it.

There are many existing retrieval and question answering datasets. However, most of them either focus on ranked list evaluation or single-candidate question answering. This divide makes it challenging to properly evaluate approaches concerned with ranking documents and providing snippets or answers for a given query. In this work, we present FiRA: a novel dataset of Fine-Grained Relevance Annotations. We extend the ranked retrieval annotations of the Deep Learning track of TREC 2019 with passage and word level graded relevance annotations for all relevant documents. We use our newly created data to study the distribution of relevance in long documents, as well as the attention of annotators to specific positions of the text. As an example, we evaluate the recently introduced TKL document ranking model. We find that although TKL exhibits state-of-the-art retrieval results for long documents, it misses many relevant passages.

The effective extraction of ranked disease-symptom relationships is a critical component in various medical tasks, including computer-assisted medical diagnosis or the discovery of unexpected associations between diseases. While existing disease-symptom relationship extraction methods are used as the foundation in the various medical tasks, no collection is available to systematically evaluate the performance of such methods. In this paper, we introduce the D isease- S ymptom R elation Collection ( dsr -collection), created by five physicians as expert annotators. We provide graded symptom judgments for diseases by differentiating between relevant symptoms and primary symptoms . Further, we provide several strong baselines, based on the methods used in previous studies. The first method is based on word embeddings, and the second on co-occurrences of MeSH-keywords of medical articles. For the co-occurrence method, we propose an adaption in which not only keywords are considered, but also the full text of medical articles. The evaluation on the dsr -collection shows the effectiveness of the proposed adaption in terms of nDCG, precision, and recall.

The verification of Extended Entity Relationship (EER) diagrams and other conceptual models that capture the design of information systems is crucial to ensure reliable systems. To scale up verification processes to larger groups of experts, Human Computation techniques were used focusing primarily on closed tasks, which constrain the number and variety of reported defects in favor of easy aggregation of derived judgements. To address this limitation of closed tasks, in this paper, we investigate EER verification (as instance of a broader family of model verification problems) with open tasks to extend the range of collected results. We also address the challenge of aggregating results of open tasks by proposing a follow-up HC task for defect validation. We evaluate our approach for HC-based EER Verification with open tasks in a set of experiments conducted with junior developers and show that (1) open tasks allow collecting a variety of insights that go beyond a manually built gold standard while still leading to good performance (F1=60%) and (2) HC-based validation can be reliably used for validating the results of open tasks (F1=84% compared to expert validation).

The success of crowdsourcing based annotation of text corpora depends on ensuring that crowdworkers are sufficiently well-trained to perform the annotation task accurately. To that end, a frequent approach to train annotators is to provide instructions and a few example cases that demonstrate how the task should be performed (referred to as the CONTROL approach). These globally defined "task-level examples", however, (i) often only cover the common cases that are encountered during an annotation task; and (ii) require effort from crowdworkers during the annotation process to find the most relevant example for the currently annotated sample. To overcome these limitations, we propose to support workers in addition to task-level examples, also with "task-instance level" examples that are semantically similar to the currently annotated data sample (referred to as Dynamic Examples for Annotation, DEXA). Such dynamic examples can be retrieved from collections previously labeled by experts, which are usually available as gold standard dataset. We evaluate DEXA on a complex task of annotating participants, interventions, and outcomes (known as PIO) in sentences of medical studies. The dynamic examples are retrieved using BioSent2Vec, an unsupervised semantic sentence similarity method specific to the biomedical domain. Results show that (i) workers of the DEXA approach reach on average much higher agreements (Cohen's Kappa) to experts than workers of the the CONTROL approach (avg. of 0.68 to experts in DEXA vs. 0.40 in CONTROL); (ii) already three per majority voting aggregated annotations of the DEXA approach reach substantial agreements to experts of 0.78/0.75/0.69 for P/I/O (in CONTROL 0.73/0.58/0.46). Finally, (iii) we acquire explicit feedback from workers and show that in the majority of cases (avg. 72%) workers find the dynamic examples useful.

The most commonly used active learning criterion is uncertainty sampling, where a supervised model is used to predict uncertain samples that should be labeled next by a human annotator. When using active learning, two problems are encountered: First, the supervised model has a cold-start problem in the beginning of the active learning process, and second, the human annotators might make labeling mistakes. In my Ph.D. research, I address the two problems with the development of an unsupervised method for the computation of informative samples. The informative samples are first manually labeled and then used for both: The training of the initial active learning model and the training of the human annotators in form of a learning-by-doing session. The planned unsupervised method will be based on word-embeddings and it will be limited to the area of text classification.