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Found 38 papers, 13 papers with code
Decoding Part-of-Speech from Human EEG Signals
This work explores techniques to predict Part-of-Speech (PoS) tags from neural signals measured at millisecond resolution with electroencephalography (EEG) during text reading.
Multi-Step Dialogue Workflow Action Prediction
In task-oriented dialogue, a system often needs to follow a sequence of actions, called a workflow, that complies with a set of guidelines in order to complete a task.
HeaP: Hierarchical Policies for Web Actions using LLMs
Large language models (LLMs) have demonstrated remarkable capabilities in performing a range of instruction following tasks in few and zero-shot settings.
On the Effectiveness of Offline RL for Dialogue Response Generation
A common training technique for language models is teacher forcing (TF).
Long-term Control for Dialogue Generation: Methods and Evaluation
This setting requires a model to not only consider the generation of these control words in the immediate context, but also produce utterances that will encourage the generation of the words at some time in the (possibly distant) future.
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data.
Ranked #3 on
Named Entity Recognition (NER)
on SLUE
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification
We address the zero-shot NERC specific challenge that the not-an-entity class is not well defined as different entity classes are considered in training and testing.
Focus Attention: Promoting Faithfulness and Diversity in Summarization
Professional summaries are written with document-level information, such as the theme of the document, in mind.
Planning with Learned Entity Prompts for Abstractive Summarization
Moreover, we demonstrate empirically that planning with entity chains provides a mechanism to control hallucinations in abstractive summaries.
Stepwise Extractive Summarization and Planning with Structured Transformers
We propose encoder-centric stepwise models for extractive summarization using structured transformers -- HiBERT and Extended Transformers.
RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble
In this paper, we report the results of our participation in the TREC-COVID challenge.
BIOMRC: A Dataset for Biomedical Machine Reading Comprehension
Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better.
On Faithfulness and Factuality in Abstractive Summarization
It is well known that the standard likelihood training and approximate decoding objectives in neural text generation models lead to less human-like responses for open-ended tasks such as language modeling and story generation.
Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generation
The question generation system is trained on general domain data, but is applied to documents in the targeted domain.
QURIOUS: Question Generation Pretraining for Text Generation
Recent trends in natural language processing using pretraining have shifted focus towards pretraining and fine-tuning approaches for text generation.
Measuring Domain Portability and ErrorPropagation in Biomedical QA
The core of our systems are based on BERT QA models, specifically the model of \cite{alberti2019bert}.
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e. g., text describing the nodes.
AUEB at BioASQ 6: Document and Snippet Retrieval
We present AUEB's submissions to the BioASQ 6 document and snippet retrieval tasks (parts of Task 6b, Phase A).
Deep Relevance Ranking Using Enhanced Document-Query Interactions
We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016).
Ranked #7 on
Ad-Hoc Information Retrieval
on TREC Robust04
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings
In this paper, we investigate models that use recurrent neural networks with sentence-level context for initial character and word-based representations.
Ranked #2 on
Part-Of-Speech Tagging
on Penn Treebank
Natural Language Processing with Small Feed-Forward Networks
We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models.
Universal Dependencies v1: A Multilingual Treebank Collection
Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments.
Static and Dynamic Feature Selection in Morphosyntactic Analyzers
Experiments on five languages show that feature selection can result in more compact models as well as higher accuracy under all conditions, but also that a dynamic ordering works better than a static ordering and that joint systems benefit more than standalone taggers.
Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning
Morpho-syntactic lexicons provide information about the morphological and syntactic roles of words in a language.
Token and Type Constraints for Cross-Lingual Part-of-Speech Tagging
We consider the construction of part-of-speech taggers for resource-poor languages.
A Universal Part-of-Speech Tagset
To facilitate future research in unsupervised induction of syntactic structure and to standardize best-practices, we propose a tagset that consists of twelve universal part-of-speech categories.
Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models
Training conditional maximum entropy models on massive data requires significant time and computational resources.
