Compared to a KB-QA system using a state-of-the-art semantic parser , our method achieves better results.

Most previous systems tackle this task in a cascaded manner: First, the input question is transformed into its meaning representation (MR) by an independent semantic parser (Zettlemoyer and Collins, 2005; Mooney, 2007; Artzi and Zettlemoyer, 2011; Liang et al., 2011; Cai and Yates,

Unlike existing KB-QA systems which treat semantic parsing and answer retrieval as two cascaded tasks, this paper presents a unified framework that can integrate semantic parsing into the question answering procedure directly.

The contributions of this work are twofold: (1) We propose a translation-based KB-QA method that integrates semantic parsing and QA in one unified framework.

A central challenge in semantic parsing is handling the myriad ways in which knowledge base predicates can be expressed.

Traditionally, semantic parsers are trained primarily from text paired with knowledge base information.

In this paper, we turn semantic parsing on its head.

We consider the semantic parsing problem of mapping natural language utterances into logical forms to be executed on a knowledge base (KB) (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Wong and Mooney, 2007; Kwiatkowski et al., 2010).

Scaling semantic parsers to large knowledge bases has attracted substantial attention recently (Cai and Yates, 2013; Berant et al., 2013; Kwiatkowski et al., 2013), since it drives applications such as question answering (QA) and information extraction (IE).

Semantic parsers need to somehow associate natural language phrases with logical predicates, e.g., they must learn that the constructions “What

We present an approach to training a joint syntactic and semantic parser that combines syntactic training information from CCGbank with semantic training information from a knowledge base via distant supervision.

We suggest that a large populated knowledge base should play a key role in syntactic and semantic parsing : in training the parser, in resolving syntactic ambiguities when the trained parser is applied to new text, and in its output semantic representation.

This paper presents an approach to training a joint syntactic and semantic parser using a large background knowledge base.

We demonstrate our approach by training a joint syntactic and semantic parser , which we call ASP.

Given these resources, the algorithm described in this section produces parameters 6 for a semantic parser .

The features are designed to share syntactic information about a word across its distinct semantic realizations in order to transfer syntactic information from CCGbank to semantic parsing .

This paper combines two lines of prior work: broad coverage syntactic parsing with CCG and semantic parsing .

Meanwhile, work on semantic parsing has focused on producing semantic parsers for answering simple natural language questions (Zelle and Mooney, 1996; Ge and Mooney, 2005; Wong and Mooney, 2006; Wong and Mooney, 2007; Lu et al., 2008; Kate and Mooney, 2006; Zettlemoyer and Collins, 2005; Kwiatkowski et al., 2011).

Finally, some work has looked at applying semantic parsing to answer queries against large knowledge bases, such as YAGO (Yahya et al., 2012) and Freebase (Cai and Yates, 2013b; Cai and Yates, 2013a; Kwiatkowski et al., 2013; Be-rant et al., 2013).

We present an approach for learning context-dependent semantic parsers to identify and interpret time expressions.

Both detection (Section 5) and resolution (Section 6) rely on the semantic parser to identify likely mentions and resolve them within context.

In this paper, we present the first context-dependent semantic parsing approach for learning to identify and interpret time expressions, addressing all three challenges.

Recently, methods for learning probabilistic semantic parsers have been shown to address such limitations (Angeli et al., 2012; Angeli and Uszkoreit, 2013).

We propose to use a context-dependent semantic parser for both detection and resolution of time expressions.

Semantic parsers map sentences to logical representations of their underlying meaning, e. g., Zelle

introduced the idea of learning semantic parsers to resolve time expressions (Angeli et al., 2012) and showed that the approach can generalize to multiple languages (Angeli and Uszkoreit, 2013).

Similarly, Bethard demonstrated that a hand-engineered semantic parser is also effective (Bethard, 2013b).

(2012), who introduced semantic parsing for this task.

We show how to generate responses by grounding SMT in the task of executing a semantic parse of a translated query against a database.

Experiments on the GEOQUERY database show an improvement of about 6 points in Fl-score for response-based learning over learning from references only on returning the correct answer from a semantic parse of a translated query.

In this paper, we present a proof-of-concept of our ideas of embedding SMT into simulated world environments as used in semantic parsing .

Embedding SMT in a semantic parsing scenario means to define translation quality by the ability of a semantic parser to construct a meaning representation from the translated query, which returns the correct answer when executed against the database.

The diagram in Figure 1 gives a sketch of response-based learning from semantic parsing in the geographical domain.

In this paper, we propose a novel approach for learning and evaluation in statistical machine translation (SMT) that borrows ideas from response-based learning for grounded semantic parsing .

Building on prior work in grounded semantic parsing, we generate translations of queries, and receive feedback by executing semantic parses of translated queries against the database.

Successful response is defined as receiving the same answer from the semantic parses for the translation and the original query.

For example, in semantic parsing , the learning goal is to produce and successfully execute a meaning representation.

Recent attempts to learn semantic parsing from question-answer pairs without recurring to annotated logical forms have been presented by Kwiatowski et al.

The algorithms presented in these works are variants of structured prediction that take executability of semantic parses into account.

Answering natural language questions using the Freebase knowledge base has recently been explored as a platform for advancing the state of the art in open domain semantic parsing .

Finally, the KB community has developed other means for QA without semantic parsing (Lopez et al., 2005; Frank et al., 2007; Unger et al., 2012; Yahya et al., 2012; Shekarpour et al., 2013).

We hope that this result establishes a new baseline against which semantic parsing researchers can measure their progress towards deeper language understanding and answering of human questions.

One question of interest is whether our system, aided by the massive web data, can be fairly compared to the semantic parsing approaches (note that Berant et al.

The AI community has tended to approach this problem with a focus on first understanding the intent of the question, Via shallow or deep forms of semantic parsing (c.f.

bounded by the accuracy of the original semantic parsing , and the well-formedness of resultant database queries.1

Researchers in semantic parsing have recently explored QA over Freebase as a way of moving beyond closed domains such as GeoQuery (Tang and Mooney, 2001).

We first applied the semantic parser and coreference classifier as described in Section 4.1 to process each dialogue, and then built a graph representation based on the automatic processing results at the end of the dialogue.

Our system first processes the data using automatic semantic parsing and coreference resolution.

For semantic parsing , we use a rule-based CCG parser (Bozsahin et al., 2005) to parse each utterance into a formal semantic representation.

Based on the semantic parsing and pairwise coreference resolution results, our system further builds a graph representation to capture the collaborative discourse and formulate referential grounding as a probabilistic labeling problem, as described next.

These works have provided valuable insights on how to manually and/or automatically build key components (e.g., semantic parsing , grounding functions between visual features and words, mapping procedures) for a situated referential grounding system.

MEANT is easily portable to other languages, requiring only an automatic semantic parser and a large monolingual corpus in the output language for identifying the semantic structures and the lexical similarity between the semantic role fillers of the reference and translation.

Apply an input language automatic shallow semantic parser to the foreign input and an output language automatic shallow semantic parser totheMToutput.

(Figure 2 shows examples of automatic shallow semantic parses on both foreign input and MT output.

To aggregate individual lexical translation probabilities into phrasal similarities between cross-lingual semantic role fillers, we compared two natural approaches to generalizing MEANT’s method of comparing semantic parses , as described below.

The first natural approach is to extend MEANT’s f-score based method of aggregating semantic parse accuracy, so as to also apply to aggregat-

Semantic parsing is the problem of mapping natural language strings into meaning representations.

1To date, a graph transducer-based semantic parser has not been published, although the Bolinas toolkit (http://WWW.isi.eolu/publications/ licensed— sw/bolinas /) contains much of the necessary infrastructure.

comparison of these two approaches is beyond the scope of this paper, we emphasize that—as has been observed with dependency parsing—a diversity of approaches can shed light on complex problems such as semantic parsing .

While all semantic parsers aim to transform natural language text to a formal representation of its meaning, there is wide variation in the meaning representations and parsing techniques used.

In contrast, semantic dependency parsing—in which the vertices in the graph correspond to the words in the sentence—is meant to make semantic parsing feasible for broader textual domains.