In this work, we present a word embedding model that learns cross-sentence dependency for improving end-to-end co-reference resolution (E2E-CR). While the traditional E2E-CR model generates word representations by running long short-term memory (LSTM) recurrent neural networks on each sentence of an input article or conversation separately, we propose linear sentence linking and attentional sentence linking models to learn cross-sentence dependency. Both sentence linking strategies enable the LSTMs to make use of valuable information from context sentences while calculating the representation of the current input word. With this approach, the LSTMs learn word embeddings considering knowledge not only from the current sentence but also from the entire input document. Experiments show that learning cross-sentence dependency enriches information contained by the word representations, and improves the performance of the co-reference resolution model compared with our baseline.

The collection and transcription of speech data is typically an expensive and time-consuming task. Voice over IP and cloud computing are poised to greatly reduce this impediment to research on spoken language interfaces in many domains. This paper documents our efforts to deploy speech-enabled web interfaces to large audiences over the Internet via Amazon Mechanical Turk, an online marketplace for work. Using the open source WAMI Toolkit, we collected corpora in two different domains which collectively constitute over 113 hours of speech. The first corpus contains 100,000 utterances of read speech, and was collected by asking workers to record street addresses in the United States. For the second task, we collected conversations with FlightBrowser, a multimodal spoken dialogue system. The FlightBrowser corpus obtained contains 10,651 utterances composing 1,113 individual dialogue sessions from 101 distinct users. The aggregate time spent collecting the data for both corpora was just under two weeks. At times, our servers were logging audio from workers at rates faster than real-time. We describe the process of collection and transcription of these corpora while providing an analysis of the advantages and limitations to this data collection method.