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Found 19 papers, 8 papers with code
Improving Multilingual Neural Machine Translation with Auxiliary Source Languages
Multilingual neural machine translation models typically handle one source language at a time.
Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization
Query-focused summaries of foreign-language, retrieved documents can help a user understand whether a document is actually relevant to the query term.
GRIM: GRaph-based Interactive narrative visualization for gaMes
\textbf{GRIM}, a prototype \textbf{GR}aph-based \textbf{I}nteractive narrative visualization system for ga\textbf{M}es, generates a rich narrative graph with branching storylines that match a high-level narrative description and constraints provided by the designer.
Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling
We introduce Reprompting, an iterative sampling algorithm that searches for the Chain-of-Thought (CoT) recipes for a given task without human intervention.
Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection
Neural sequence generation models are known to "hallucinate", by producing outputs that are unrelated to the source text.
Recognition and Co-Analysis of Pedestrian Activities in Different Parts of Road using Traffic Camera Video
The goal for this research is to understand the correlation between bus stop locations and mid-block crossings, so as to assist traffic engineers in implementing Vision Zero strategies to improve pedestrian safety.
Rule-based Morphological Inflection Improves Neural Terminology Translation
Current approaches to incorporating terminology constraints in machine translation (MT) typically assume that the constraint terms are provided in their correct morphological forms.
Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer
Multilingual pre-trained contextual embedding models (Devlin et al., 2019) have achieved impressive performance on zero-shot cross-lingual transfer tasks.
How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation?
While non-autoregressive (NAR) models are showing great promise for machine translation, their use is limited by their dependence on knowledge distillation from autoregressive models.
EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints
We introduce an Edit-Based Transformer with Repositioning (EDITOR), which makes sequence generation flexible by seamlessly allowing users to specify preferences in output lexical choice.
Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation
While Iterative Back-Translation and Dual Learning effectively incorporate monolingual training data in neural machine translation, they use different objectives and heuristic gradient approximation strategies, and have not been extensively compared.
Convolutional Neural Network Training with Distributed K-FAC
Training neural networks with many processors can reduce time-to-solution; however, it is challenging to maintain convergence and efficiency at large scales.
Flexible Non-Autoregressive Neural Machine Translation via Repositioning Edit Operations
We introduce an iterative text refinement model to reduce the decoding space of non-autoregressive models by disentangling the token prediction and relative position prediction.
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus.
HPC AI500: A Benchmark Suite for HPC AI Systems
In this paper, we propose HPC AI500 --- a benchmark suite for evaluating HPC systems that running scientific DL workloads.
Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation
Despite some empirical success at correcting exposure bias in machine translation, scheduled sampling algorithms suffer from a major drawback: they incorrectly assume that words in the reference translations and in sampled sequences are aligned at each time step.
Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation
We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation (NMT).
The University of Maryland's Chinese-English Neural Machine Translation Systems at WMT18
This paper describes the University of Maryland{'}s submission to the WMT 2018 Chinese↔English news translation tasks.
