Abstract:
This thesis is organised in two parts. First, an explainability in Information retrieval
(IR) research where we focus on the performance of the IR models. We present a toolkit
I-REX to illustrate the performance and explainability of IR systems. It is an interactive
interface built on top of Lucene and gives a white box view of any proposed method. It
is implemented as a web based and as well as shell based interface to provide an intuitive
explanations and performance of IR systems. The baseline retrieval models such as LM,
BM25 and DFR, and a set of well-de ned features enable debugging the performance
of retrieval experiments such as ad-hoc IR or query expansion. Next we worked on an
open domain complex factoid Question Answering (QA). Creating annotated data in QA
problem requires lot of resources and it is very time consuming. The available datasets
are often domain speci c and most of the times created for some speci c languages.
Therefore we mainly focus on answering the questions in an unsupervised way. As a
benchmark data we used the data provided by Lu et al. (Quest)[26]. It mainly focuses on
complex questions which cannot be answered by knowledge graphs (KGs) directly. Our
architecture uses corpus signals over the various documents along with the traditional
QA pipeline to answer the complex questions. We proposed a set of modi ed evaluation
protocols to overcome some serious pitfalls in the evaluation measure used in Quest.
We also compared the performances of our architecture with another neural benchmark
model DrQA [11]. Experiments on this benchmark datasets have shown that our model
signi cantly outperforms Quest and DrQA. We nd this very encouraging since DrQA
is trained on SQuAD [32], TREC Questions [4], WebQuestion [5], WikiMovies [30] while
our proposed method is unsupervised in nature.
