Causality understanding between events is a critical natural language processing task that
is helpful in many areas, including health care, business risk management, and finance. On …

We present a method for the automatic extraction of glossary terms from unconstrained natural
language requirements. The glossary terms are identified in two steps - a) compute units (…

… : (4) R t = Π j = 1 j = n d ( e ( C j , P , S j , B j ) , R t [ 0 : j ] ) where n is the number of words in
the decoded sequence ( R t ) and R t [ j ] is j th word of the generated sequence. Here, R t [ 0 ] …

Early identification of Non-Functional Requirements (NFRs) is important as this has direct
bearing on the design and architecture of the system. NFRs form the basis for architects to …

… falls under the episodic RL problem where each episode consists of: where s n indicates
current dialogue state, gs n indicates current GDM state, a n represents the action and r n …

One of the fundamental challenges for information officers of most organizations today is the
growing number of cyber security threats. This has led to an emerging field of Cyber Threat …

… The virtual agent always attempts to improve transformation ratio which is defined as
follows: (1) T r a n s f o r m a t i o n r a t i o = C I where C is the number of dialogues that ended …

Causality understanding between events is a critical natural language processing task that
is helpful in many areas, including health care, business risk management and finance. On …

… We also report the Perplexity (PPL) and the average response length (R-LEN) of the … Further,
we obtain a score of R-LEN = 39.93 is also better than that of ARDM, ARDM+BK, ARDM+In, …

… models which assign a score s = ϕ(h, r, t) ∈ R to the predicted links and validate whether the
link … The edges of the graph represent the relations r ∈ R. The core relation path is the path …