This paper proposes a novel maximum entropy based rule selection (MERS) model for syntax-based statistical machine translation (SMT). The MERS model combines local contextual information around rules and information of sub-trees covered by variables in rules. Therefore, our model allows the decoder to perform context-dependent rule selection during decoding. We incorporate the MERS model into a...

(74) Attorney, Agent, or Firm * Aaron Chatterjee; Andrew Sanders; Micky Minhas (57) ABSTRACT Techniques and tools for reducing latency in video encoding and decoding by constraining latency due to reordering of video frames, and by indicating the constraint on frame reor dering latency with one or more syntax elements that accom pany encoded data for the video frames. For example, a real time c...

Inspired by previous preprocessing approaches to SMT, this paper proposes a novel, probabilistic approach to reordering which combines the merits of syntax and phrase-based SMT. Given a source sentence and its parse tree, our method generates, by tree operations, an n-best list of reordered inputs, which are then fed to standard phrase-based decoder to produce the optimal translation. Experimen...

We propose an elastic pipeline that can apply dynamic voltage scaling (DVS) to hardwired logic circuits. In order to demonstrate its feasibility, a hardwired H.264/AVC HDTV decoder is designed as a real-time application. An entropy decoding process is divided into context-based adaptive binary arithmetic coding (CABAC) and syntax element decoding (SED), which has advantages of smoothing workloa...

We describe the version six release of Joshua, an open-source statistical machine translation toolkit. The main difference from release five is the introduction of a simple, unlexicalized, phrase-based stack decoder. This phrase-based decoder shares a hypergraph format with the syntax-based systems, permitting a tight coupling with the existing codebase of feature functions and hypergraph tools...

We describe the version six release of Joshua, an open-source statistical machine translation toolkit. The main difference from release five is the introduction of a simple, unlexicalized, phrase-based stack decoder. This phrase-based decoder shares a hypergraph format with the syntax-based systems, permitting a tight coupling with the existing codebase of feature functions and hypergraph tools...

Tree-based translation models, which exploit the linguistic syntax of source language, usually separate decoding into two steps: parsing and translation. Although this separation makes tree-based decoding simple and efficient, its translation performance is usually limited by the number of parse trees offered by parser. Alternatively, we propose to parse and translate jointly by casting tree-ba...

This paper describes a method of interactively visualizing and directing the process of translating a sentence. The method allows a user to explore a model of syntax-based statistical machine translation (MT), to understand the model’s strengths and weaknesses, and to compare it to other MT systems. Using this visualization method, we can find and address conceptual and practical problems in an...

Transformer-based neural machine translation (NMT) has achieved state-of-the-art performance in the NMT paradigm. This method assumes that model can automatically learn linguistic knowledge (e.g., grammar and syntax) from parallel corpus via an attention network. However, network cannot capture deep internal structure of a sentence. Therefore, it is natural to introduce some prior guide model. ...

Multi-part words in English language are hyphenated and hyphen is used to separate different parts. Persian language consists of multi-part words as well. Based on Persian morphology, half-space character is needed to separate parts of multi-part words where in many cases people incorrectly use space character instead of half-space character. This common incorrectly use of space leads to some s...