We propose a domain specific Question Answering system. We deviate from approaching this problem as a Textual Entailment task. We implemented a Memory Network-based Question Answering system which test a Machine’s understanding of legal text and identifies whether an answer to a question is correct or wrong, given some background knowledge. We also prepared a corpus of real USA MBE Bar exams fo...

We participated in Japanese tasks of RITE in NTCIR9 (team id: “KYOTO”). Our proposed method regards predicateargument structure as a basic unit of handling the meaning of text/hypothesis, and performs the matching between text and hypothesis. Our system first performs predicateargument structure analysis to both a text and a hypothesis. Then, we perform the matching between text and hypothesis....

This paper presents a machine-learning approach for the recognition of textual entailment. For our approach we model lexical and semantic features. We study the effect of stacking and voting joint classifier combination techniques which boost the final performance of the system. In an exhaustive experimental evaluation, the performance of the developed approach is measured. The obtained results...

We address two challenges for automatic machine translation evaluation: a) avoiding the use of reference translations, and b) focusing on adequacy estimation. From an economic perspective, getting rid of costly hand-crafted reference translations (a) permits to alleviate the main bottleneck in MT evaluation. From a system evaluation perspective, pushing semantics into MT (b) is a necessity in o...

The textual entailment recognition system that we discuss in this paper represents a perspective-based approach composed of two modules that analyze text-hypothesis pairs from a strictly lexical and syntactic perspectives, respectively. We attempt to prove that the textual entailment recognition task can be overcome by performing individual analysis that acknowledges us of the maximum amount of...

In this paper we present two original methods for recognizing textual inference.First one is a modified resolution method such that some linguistic considerations are introduced in the unification of two atoms. The approach is possible due to the recent methods of transforming texts in logic formulas. Second one is based on semantic relations in text, as presented in WordNet. Some similarities ...

This paper describes the THK system that participated in the BC subtask, MC subtask, ExamBC subtask and UnitTest in NTCIR-10 RITE-2. Our system learns plausible transformations of pairs of Text t1 and Hypothesis t2 only from semantic labels of the pairs using a discriminative probabilistic model combined with the framework of Natural Logic. The model is trained so as to prefer alignments and th...

We define a novel textual entailment task that requires inference over multiple premise sentences. We present a new dataset for this task that minimizes trivial lexical inferences, emphasizes knowledge of everyday events, and presents a more challenging setting for textual entailment. We evaluate several strong neural baselines and analyze how the multiple premise task differs from standard tex...

In this paper, we describe our submission to the Shared Task #1. We tried to follow the underlying idea of the task, that is, evaluating the gap of full-fledged recognizing textual entailment systems with respect to compositional distributional semantic models (CDSMs) applied to this task. We thus submitted two runs: 1) a system obtained with a machine learning approach based on the feature spa...

This paper introduces BIUTEE1, an opensource system for recognizing textual entailment. Its main advantages are its ability to utilize various types of knowledge resources, and its extensibility by which new knowledge resources and inference components can be easily integrated. These abilities make BIUTEE an appealing RTE system for two research communities: (1) researchers of end applications,...