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Found 33 papers, 12 papers with code
Morphological Inflection Generation with Hard Monotonic Attention
We present a neural model for morphological inflection generation which employs a hard attention mechanism, inspired by the nearly-monotonic alignment commonly found between the characters in a word and the characters in its inflection.
Towards String-to-Tree Neural Machine Translation
We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees.
Split and Rephrase: Better Evaluation and a Stronger Baseline
To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8. 68 BLEU and fostering further progress on the task.
Split and Rephrase: Better Evaluation and Stronger Baselines
To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8. 68 BLEU and fostering further progress on the task.
Massively Multilingual Neural Machine Translation
Our experiments on a large-scale dataset with 102 languages to and from English and up to one million examples per direction also show promising results, surpassing strong bilingual baselines and encouraging future work on massively multilingual NMT.
Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injection
When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must "guess" this missing information, often leading to incorrect translations in the given context.
The Missing Ingredient in Zero-Shot Neural Machine Translation
Multilingual Neural Machine Translation (NMT) models are capable of translating between multiple source and target languages.
Filling Gender \& Number Gaps in Neural Machine Translation with Black-box Context Injection
When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must {``}guess{''} this missing information, often leading to incorrect translations in the given context.
Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets
Phenomenon-specific "adversarial" datasets have been recently designed to perform targeted stress-tests for particular inference types.
Unsupervised Domain Clusters in Pretrained Language Models
The notion of "in-domain data" in NLP is often over-simplistic and vague, as textual data varies in many nuanced linguistic aspects such as topic, style or level of formality.
Real-Time Sign Language Detection using Human Pose Estimation
We propose a lightweight real-time sign language detection model, as we identify the need for such a case in videoconferencing.
KoBE: Knowledge-Based Machine Translation Evaluation
Our approach achieves the highest correlation with human judgements on 9 out of the 18 language pairs from the WMT19 benchmark for evaluation without references, which is the largest number of wins for a single evaluation method on this task.
$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
TRUE: Re-evaluating Factual Consistency Evaluation
Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability.
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering
Question answering models commonly have access to two sources of "knowledge" during inference time: (1) parametric knowledge - the factual knowledge encoded in the model weights, and (2) contextual knowledge - external knowledge (e. g., a Wikipedia passage) given to the model to generate a grounded answer.
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development.
Multilingual Sequence-to-Sequence Models for Hebrew NLP
We demonstrate that by casting tasks in the Hebrew NLP pipeline as text-to-text tasks, we can leverage powerful multilingual, pretrained sequence-to-sequence models as mT5, eliminating the need for a specialized, morpheme-based, separately fine-tuned decoder.
mFACE: Multilingual Summarization with Factual Consistency Evaluation
Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets.
q2d: Turning Questions into Dialogs to Teach Models How to Search
One of the exciting capabilities of recent language models for dialog is their ability to independently search for relevant information to ground a given dialog response.
Surfacing Biases in Large Language Models using Contrastive Input Decoding
We use CID to highlight context-specific biases that are hard to detect with standard decoding strategies and quantify the effect of different input perturbations.
What You See is What You Read? Improving Text-Image Alignment Evaluation
Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks.
Ranked #11 on
Visual Reasoning
on Winoground
TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries.
SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation
In this work, we introduce SEAHORSE, a dataset for multilingual, multifaceted summarization evaluation.
Evaluating and Modeling Attribution for Cross-Lingual Question Answering
Based on these models, we improve the attribution level of a cross-lingual question-answering system.
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input.
Abstractive Text Summarization
Natural Language Inference
+2
A Comprehensive Evaluation of Tool-Assisted Generation Strategies
A growing area of research investigates augmenting language models with tools (e. g., search engines, calculators) to overcome their shortcomings (e. g., missing or incorrect knowledge, incorrect logical inferences).
Multilingual Instruction Tuning With Just a Pinch of Multilinguality
As instruction-tuned large language models (LLMs) gain global adoption, their ability to follow instructions in multiple languages becomes increasingly crucial.
Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers
In this work, we propose GRANOLA QA, a novel evaluation setting where a predicted answer is evaluated in terms of accuracy and informativeness against a set of multi-granularity answers.
A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains
REVEAL includes comprehensive labels for the relevance, attribution to evidence passages, and logical correctness of each reasoning step in a language model's answer, across a variety of datasets and state-of-the-art language models.
MiMiC: Minimally Modified Counterfactuals in the Representation Space
We demonstrate the effectiveness of the proposed approaches in mitigating bias in multiclass classification and in reducing the generation of toxic language, outperforming strong baselines.
Q^{2}: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
Abstractive Text Summarization
Natural Language Inference
+3
