Dan Goldwasser

AAAI Conference 2019 Conference Paper

TransConv: Relationship Embedding in Social Networks

• Yi-Yu Lai
• Jennifer Neville
• Dan Goldwasser

Representation learning (RL) for social networks facilitates real-world tasks such as visualization, link prediction and friend recommendation. Traditional knowledge graph embedding models learn continuous low-dimensional embedding of entities and relations. However, when applied to social networks, existing approaches do not consider the rich textual communications between users, which contains valuable information to describe social relationships. In this paper, we propose TransConv, a novel approach that incorporates textual interactions between pair of users to improve representation learning of both users and relationships. Our experiments on real social network data show TransConv learns better user and relationship embeddings compared to other state-of-theart knowledge graph embedding models. Moreover, the results illustrate that our model is more robust for sparse relationships where there are fewer examples.

AAAI Conference 2018 Conference Paper

FEEL: Featured Event Embedding Learning

• I-Ta Lee
• Dan Goldwasser

Statistical script learning is an effective way to acquire world knowledge which can be used for commonsense reasoning. Statistical script learning induces this knowledge by observing event sequences generated from texts. The learned model thus can predict subsequent events, given earlier events. Recent approaches rely on learning event embeddings which capture script knowledge. In this work, we suggest a general learning model–Featured Event Embedding Learning (FEEL)–for injecting event embeddings with fine grained information. In addition to capturing the dependencies between subsequent events, our model can take into account higher level abstractions of the input event which help the model generalize better and account for the global context in which the event appears. We evaluated our model over three narrative cloze tasks, and showed that our model is competitive with the most recent state-of-the-art. We also show that our resulting embedding can be used as a strong representation for advanced semantic tasks such as discourse parsing and sentence semantic relatedness.

AAAI Conference 2016 Conference Paper

Ask, and Shall You Receive? Understanding Desire Fulfillment in Natural Language Text

• Snigdha Chaturvedi
• Dan Goldwasser
• Hal Daume III

The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding. This paper introduces the task of identifying if a desire expressed by a subject in a given short piece of text was fulfilled. We propose various unstructured and structured models that capture fulfillment cues such as the subject’s emotional state and actions. Our experiments with two different datasets demonstrate the importance of understanding the narrative and discourse structure to address this task.

AAAI Conference 2014 Conference Paper

Learning Latent Engagement Patterns of Students in Online Courses

• Arti Ramesh
• Dan Goldwasser
• Bert Huang
• Hal Daume III
• Lise Getoor

Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement will help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interaction with the MOOC open up avenues for studying student engagement at scale. In this work, we develop a framework for modeling and understanding student engagement in online courses based on student behavioral cues. Our first contribution is the abstraction of student engagement types using latent representations. We use that abstraction in a probabilistic model to connect student behavior with course completion. We demonstrate that the latent formulation for engagement helps in predicting student survival across three MOOCs. Next, in order to initiate better instructor interventions, we need to be able to predict student survival early in the course. We demonstrate that we can predict student survival early in the course reliably using the latent model. Finally, we perform a closer quantitative analysis of user interaction with the MOOC and identify student activities that are good indicators for survival at different points in the course.

IJCAI Conference 2011 Conference Paper

Learning from Natural Instructions

• Dan Goldwasser
• Dan Roth

Machine learning is traditionally formalized and researched as the study of learning concepts and decision functions from labeled examples, requiring a representation that encodes information about the domain of the decision function to be learned. We are interested in providing a way for a human teacher to interact with an automated learner using natural instructions, thus allowing the teacher to communicate the relevant domain expertise to the learner without necessarily knowing anything about the internal representations used in the learning process. In this paper we suggest to view the process of learning a decision function as a natural language lesson interpretation problem instead of learning from labeled examples. This interpretation of machine learning is motivated by human learning processes, in which the learner is given a lesson describing the target concept directly, and a few instances exemplifying it. We introduce a learning algorithm for the lesson interpretation problem that gets feedback from its performance on the final task, while learning jointly (1) how to interpret the lesson and (2) how to use this interpretation to do well on the final task. his approach alleviates the supervision burden of traditional machine learning by focusing on supplying the learner with only human-level task expertise for learning. We evaluate our approach by applying it to the rules of the Freecell solitaire card game. We show that our learning approach can eventually use natural language instructions to learn the target concept and play the game legally. Furthermore, we show that the learned semantic interpreter also generalizes to previously unseen instructions.

ICML Conference 2010 Conference Paper

Structured Output Learning with Indirect Supervision

• Ming-Wei Chang
• Vivek Srikumar
• Dan Goldwasser
• Dan Roth 0001

ECAI Conference 2006 Conference Paper

Identifying Inter-Domain Similarities Through Content-Based Analysis of Hierarchical Web-Directories

• Shlomo Berkovsky
• Dan Goldwasser
• Tsvi Kuflik
• Francesco Ricci 0001

Providing accurate personalized information services to the users requires knowing their interests and needs, as defined by their User Models (UMs). Since the quality of the personalization depends on the richness of the UMs, services would benefit from enriching their UMs through importing and aggregating partial UMs built by other services from relatively similar domains. The obvious question is how to determine the similarity of domains? This paper proposes to compute inter-domain similarities by exploiting well-known Information Retrieval techniques for comparing textual contents of the Web-sites, classified under the domain nodes in Web-directories. Initial experiments validate feasibility of the proposed approach and raise open research questions.