text: Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset levesque2011winograd . In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge.
  @cite_22: We study disambiguating of pronoun references in Winograd Schemas, which are part of the Winograd Schema Challenge, a proposed replacement for the Turing test. In particular we consider sentences where the pronoun can be resolved to both antecedents without semantic violations in world knowledge, that means for both readings of the sentence there is a possible consistent world. Nevertheless humans will strongly prefer one answer, which can be explained by pragmatic effects described in Relevance Theory. We state formal optimization criteria based on principles of Relevance Theory in a simplification of Roger Schank's graph framework for natural language understanding. We perform experiments using Answer Set Programming and report the usefulness of our criteria for disambiguation and their sensitivity to parameter variations.
  @cite_29: The Winograd Schema Challenge is an alternative to the Turing Test that may provide a more meaningful measure of machine intelligence. It poses a set of coreference resolution problems that cannot be solved without human-like reasoning. In this paper, we take the view that the solution to such problems lies in establishing discourse coherence. Specifically, we examine two types of rhetorical relations that can be used to establish discourse coherence: positive and negative correlation. We introduce a framework for reasoning about correlation between sentences, and show how this framework can be used to justify solutions to some Winograd Schema problems.
  @cite_17: Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions. One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use of background knowledge. In this paper we propose an algorithmic solution that involves a new representation for the knowledge required to address hard coreference problems, along with a constrained optimization framework that uses this knowledge in coreference decision making. Our representation, Predicate Schemas, is instantiated with knowledge acquired in an unsupervised way, and is compiled automatically into constraints that impact the coreference decision. We present a general coreference resolution system that significantly improves state-of-the-art performance on hard, Winograd-style, pronoun resolution cases, while still performing at the stateof-the-art level on standard coreference resolution datasets.
  @cite_34: Concerned about the Turing test's ability to correctly evaluate if a system exhibits human-like intelligence, the Winograd Schema Challenge (WSC) has been proposed as an alternative. A Winograd Schema consists of a sentence and a question. The answers to the questions are intuitive for humans but are designed to be difficult for machines, as they require various forms of commonsense knowledge about the sentence. In this paper we demonstrate our progress towards addressing the WSC. We present an approach that identifies the knowledge needed to answer a challenge question, hunts down that knowledge from text repositories, and then reasons with them to come up with the answer. In the process we develop a semantic parser (www.kparser.org). We show that our approach works well with respect to a subset of Winograd schemas.
  @cite_11: We examine the task of resolving complex cases of definite pronouns, specifically those for which traditional linguistic constraints on coreference (e.g., Binding Constraints, gender and number agreement) as well as commonly-used resolution heuristics (e.g., string-matching facilities, syntactic salience) are not useful. Being able to solve this task has broader implications in artificial intelligence: a restricted version of it, sometimes referred to as the Winograd Schema Challenge, has been suggested as a conceptually and practically appealing alternative to the Turing Test. We employ a knowledge-rich approach to this task, which yields a pronoun resolver that outperforms state-of-the-art resolvers by nearly 18 points in accuracy on our dataset.
summary: Previous attempts on solving the Winograd Schema Challenge usually involve heavy utilization of annotated knowledge bases, rule-based reasoning, or hand-crafted features @cite_17 @cite_29 @cite_22 . In particular, Rahman and Ng @cite_11 employ human annotators to build more supervised training data. Their model utilizes nearly 70K hand-crafted features, including querying data from Google Search API. @cite_34 rely on a semantic parser to understand the question, query texts through Google Search, and reason on the graph produced by the parser. Similarly, Sch "u ller @cite_22 formalizes the knowledge-graph data structure and a reasoning process based on cognitive linguistics theories. @cite_29 introduces a framework for reasoning, using expensive annotated knowledge bases as axioms.