Our model learns higher-level meaning representations for single words from textual and visual input in a joint fashion.

To learn meaning representations of single words from textual and visual input, we employ stacked (denoising) autoencoders (SAEs).

Then, we join these two SAEs by feeding their respective second coding simultaneously to another autoencoder, whose hidden layer thus yields the fused meaning representation .

In this paper, we presented a model that uses stacked autoencoders to learn grounded meaning representations by simultaneously combining textual and Visual modalities.

We learn meaning representations for the nouns contained in McRae et al.’s (2005) feature norms.

We used the model described above and the meaning representations obtained from the output of the bimodal latent layer for all the evaluation tasks detailed below.

Despite differences in formulation, most existing models conceptualize the problem of meaning representation as one of learning from multiple views corresponding to different modalities.

In this work, we introduce a model, illustrated in Figure 1, which learns grounded meaning representations by mapping words and images into a common embedding space.

Unlike most previous work, our model is defined at a finer level of granularity — it computes meaning representations for individual words and is unique in its use of attributes as a means of representing the textual and visual modalities.

The use of stacked autoencoders to extract a shared lexical meaning representation is new to our knowledge, although, as we explain below related to a large body of work on deep learning.

We use a Combinatory Categorial Grammar to construct compositional meaning representations , while considering contextual cues, such as the document creation time and the tense of the governing verb, to compute the final time values.

Both models used a Combinatory Categorial Grammar (CCG) to construct a set of possible temporal meaning representations .

For both tasks, we define the space of possible compositional meaning representations Z, where each 2 E Z defines a unique time expression 6.

For both tasks, we make use of a hand-engineered Combinatory Categorial Grammar (CCG) to construct a set of meaning representations that identify the time being described.

For example, this grammar maps the phrase “2nd Friday of July” to the meaning representation intersect(nth(2,friday),july), which encodes the set of all such days.

We build on a number of existing algorithmic ideas, including using CCGs to build meaning representations (Zettlemoyer and Collins, 2005; Zettlemoyer and Collins, 2007; Kwiatkowski et al., 2010; Kwiatkowski et al., 2011), building derivations to transform the output of the CCG parser based on context (Zettlemoyer and Collins, 2009), and using weakly supervised parameter updates (Artzi and Zettlemoyer, 2011; Artzi and Zettlemoyer, 2013b).

derives the notion of negation scope assumed in this task from the structure of logical-form meaning representations .

(2011), on the one hand, and the broad-coverage, MRS meaning representations of the ERG, on the other hand.

Unlike the rather complex top-performing systems from the original 2012 competition, our MRS Crawler is defined by a small set of general rules that operate over general-purpose, explicit meaning representations .

Our system implements these findings through a notion of functor-argument ‘crawling’, using as our starting point the underspecified logical-form meaning representations provided by a general-purpose deep parser.

This system operates over the normalized semantic representations provided by the LinGO English Resource Grammar (ERG; Flickinger, 2000).3 The ERG maps surface strings to meaning representations in the format of Minimal Recursion Semantics (MRS; Copestake et al., 2005).

5 In other words, a possible semantic interpretation of the (string-based) Shared Task annotation guidelines and data is in terms of a quantifier-free approach to meaning representation , or in terms of one where quantifier scope need not be made explicit (as once suggested by, among others, Alshawi, 1992).

Those efforts map questions to sophisticated meaning representations that are then attempted to be matched against Viable answer candidates in the knowledge base.

The model challenge involves finding the best meaning representation for the question, converting it into a query and executing the query on the KB.

More recent research started to minimize this direct supervision by using latent meaning representations (Berant et

We instead attack the problem of QA from a KB from an IE perspective: we learn directly the pattern of QA pairs, represented by the dependency parse of questions and the Freebase structure of answer candidates, without the use of intermediate, general purpose meaning representations .

Typically questions are converted into some meaning representation (e. g., the lambda calculus), then mapped to database queries.

Abstract Meaning Representation (AMR) is a semantic formalism for which a growing set of annotated examples is available.

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

Abstract Meaning Representation (AMR) (Banarescu et al., 2013; Dorr et al., 1998) is a semantic formalism in which the meaning of a sentence is encoded as a rooted, directed, acyclic graph.

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.

To evaluate our approach, we use the benchmark provided by the KBGen challenge (Banik et al., 2012; Banik et al., 2013), a challenge designed to evaluate generation from knowledge bases; where the input is a KB subset; and where the expected output is a complex sentence conveying the meaning represented by the input.

(Wong and Mooney, 2007) uses synchronous grammars to transform a variable free tree structured meaning representation into sentences.

dom Field to generate from the same meaning representations .

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.

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

(2010) or Goldwasser and Roth (2013) describe a response-driven learning framework for the area of semantic parsing: Here a meaning representation is “tried out” by itera-tively generating system outputs, receiving feedback from world interaction, and updating the model parameters.