Recent advances in language modeling have enabled new conversational systems.In particular, it is often desirable for people to make choices among specified options when using such systems.We address this problem of reference resolution, when people use natural expressions to choose between the entities.For example, given the choice 'Should we make a Simnel cake or a Pandan cake?' a natural response from a dialog participant may be indirect: 'let's make the green one'.Such natural expressions have been little studied for reference resolution.We argue that robustly understanding such language has large potential for improving naturalness in dialog, recommendation, and search systems.We create AltEntities 1 (Alternative Entities), a new public dataset of 42K entity pairs and expressions (referring to one entity in the pair), and develop models for the disambiguation problem.Consisting of indirect referring expressions across three domains, our corpus enables for the first time the study of how language models can be adapted to this task.We find they achieve 82%-87% accuracy in realistic settings, which while reasonable also invites further advances.

The phenomenon of reported speech -- whereby we quote the words, thoughts and opinions of others, or recount past dialogue -- is widespread in conversational speech. Detecting such quotations automatically has numerous applications: for example, in enhancing automatic transcription or spoken language understanding applications. However, the task is challenging, not least because lexical cues of quotations are frequently ambiguous or not present in spoken language. The aim of this paper is to identify potential prosodic cues of reported speech which could be used, along with the lexical ones, to automatically detect quotations and ascribe them to their rightful source, that is reconstructing their attribution relations. In order to do so we analyze SARC, a small corpus of telephone conversations that we have annotated with attribution relations. The results of the statistical analysis performed on the data show how variations in pitch, intensity, and timing features can be exploited as cues of quotations. Furthermore, we build a SVM classifier which integrates lexical and prosodic cues to automatically detect quotations in speech that performs significantly better than chance.

Direct quotations are used for opinion mining and information extraction as they have an easy to extract span and they can be attributed to a speaker with high accuracy.However, simply focusing on direct quotations ignores around half of all reported speech, which is in the form of indirect or mixed speech.This work presents the first large-scale experiments in indirect and mixed quotation extraction and attribution.We propose two methods of extracting all quote types from news articles and evaluate them on two large annotated corpora, one of which is a contribution of this work.We further show that direct quotation attribution methods can be successfully applied to indirect and mixed quotation attribution.* *These authors contributed equally to this work.by quotation marks, which makes them easy to extract.However, annotated resources suggest that direct quotations represent only a limited portion of all quotations, i.e., around 30% in the Penn Attribution Relation Corpus (PARC), which covers Wall Street Journal articles, and 52% in the Sydney Morning Herald Corpus (SMHC), with the remainder being indirect (Ex.1c) or mixed (Ex.1b)quotations.Retrieving only direct quotations can miss key content that can change the interpretation of the quotation (Ex.1b) and will entirely miss indirect quotations.