  Agents that negotiate with humans find broad applications in pedagogy and
conversational AI. Most efforts in human-agent negotiations rely on restrictive
menu-driven interfaces for communication. To advance the research in
language-based negotiation systems, we explore a novel task of early prediction
of buyer-seller negotiation outcomes, by varying the fraction of utterances
that the model can access. We explore the feasibility of early prediction by
using traditional feature-based methods, as well as by incorporating the
non-linguistic task context into a pretrained language model using sentence
templates. We further quantify the extent to which linguistic features help in
making better predictions apart from the task-specific price information.
Finally, probing the pretrained model helps us to identify specific features,
such as trust and agreement, that contribute to the prediction performance.
