  This paper addresses the task of conversational question answering (ConvQA)
over knowledge graphs (KGs). The majority of existing ConvQA methods rely on
full supervision signals with a strict assumption of the availability of gold
logical forms of queries to extract answers from the KG. However, creating such
a gold logical form is not viable for each potential question in a real-world
scenario. Hence, in the case of missing gold logical forms, the existing
information retrieval-based approaches use weak supervision via heuristics or
reinforcement learning, formulating ConvQA as a KG path ranking problem.
Despite missing gold logical forms, an abundance of conversational contexts,
such as entire dialog history with fluent responses and domain information, can
be incorporated to effectively reach the correct KG path. This work proposes a
contrastive representation learning-based approach to rank KG paths
effectively. Our approach solves two key challenges. Firstly, it allows weak
supervision-based learning that omits the necessity of gold annotations.
Second, it incorporates the conversational context (entire dialog history and
domain information) to jointly learn its homogeneous representation with KG
paths to improve contrastive representations for effective path ranking. We
evaluate our approach on standard datasets for ConvQA, on which it
significantly outperforms existing baselines on all domains and overall.
Specifically, in some cases, the Mean Reciprocal Rank (MRR) and Hit@5 ranking
metrics improve by absolute 10 and 18 points, respectively, compared to the
state-of-the-art performance.
