Reviewerly/exHarmony huggingface dataset:
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation

Self-training emerges as an important research line on domain adaptation.By taking the model's prediction as the pseudo labels of the unlabeled data, self-training bootstraps the model with pseudo instances in the target domain.However, the prediction errors of pseudo labels (label noise) challenge the performance of self-training.To address this problem, previous approaches only use reliable pseudo instances, i.e., pseudo instances with high prediction confidence, to retrain the model.Although these strategies effectively reduce the label noise, they are prone to miss the hard examples.In this paper, we propose a new self-training framework for domain adaptation, namely Domain adversarial learning enhanced Self-Training Framework (DaMSTF).Firstly, DaMSTF involves meta-learning to estimate the importance of each pseudo instance, so as to simultaneously reduce the label noise and preserve hard examples.Secondly, we design a meta constructor for constructing the meta validation set, which guarantees the effectiveness of the meta-learning module by improving the quality of the meta validation set.Thirdly, we find that the meta-learning module suffers from the training guidance vanishment and tends to converge to an inferior optimal.To this end, we employ domain adversarial learning as a heuristic neural network initialization method, which can help the meta-learning module converge to a better optimal.Theoretically and experimentally, we demonstrate the effectiveness of the proposed DaMSTF.On the cross-domain sentiment classification task, DaMSTF improves the performance of BERT with an average of nearly 4%.
CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality

There are three problems existing in the popular data-to-text datasets.First, the large-scale datasets either contain noise or lack real application scenarios.Second, the datasets close to real applications are relatively small in size.Last, current datasets bias in the English language while leaving other languages underexplored.To alleviate these limitations, in this paper, we present CATS, a pragmatic Chinese answer-to-sequence dataset with large scale and high quality.The dataset aims to generate textual descriptions for the answer in the practical TableQA system.Further, to bridge the structural gap between the input SQL and table and establish better semantic alignments, we propose a Unified Graph Transformation approach to establish a joint encoding space for the two hybrid knowledge resources and convert this task to a graph-to-text problem.The experiment results demonstrate the effectiveness of our proposed method.Further analysis on CATS 1 attests to both the high quality and challenges of the dataset.
RMLM: A Flexible Defense Framework for Proactively Mitigating Word-level Adversarial Attacks

Adversarial attacks on deep neural networks keep raising security concerns in natural language processing research.Existing defenses focus on improving the robustness of the victim model in the training stage.However, they often neglect to proactively mitigate adversarial attacks during inference.Towards this overlooked aspect, we propose a defense framework that aims to mitigate attacks by confusing attackers and correcting adversarial contexts that are caused by malicious perturbations.Our framework comprises three components: (1) a synonym-based transformation to randomly corrupt adversarial contexts in the word level, (2) a developed BERT defender to correct abnormal contexts in the representation level, and (3) a simple detection method to filter out adversarial examples, any of which can be flexibly combined.Additionally, our framework helps improve the robustness of the victim model during training.Extensive experiments demonstrate the effectiveness of our framework in defending against word-level adversarial attacks.
Multi3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue

Task-oriented dialogue (TOD) systems have been widely deployed in many industries as they deliver more efficient customer support.These systems are typically constructed for a single domain or language and do not generalise well beyond this.To support work on Natural Language Understanding (NLU) in TOD across multiple languages and domains simultaneously, we constructed MULTI 3 NLU ++ , a multilingual, multi-intent, multi-domain dataset.MULTI 3 NLU ++ extends the Englishonly NLU++ dataset to include manual translations into a range of high, medium, and low resource languages (Spanish, Marathi, Turkish and Amharic), in two domains (BANKING and HOTELS).Because of its multi-intent property, MULTI 3 NLU ++ represents complex and natural user goals, and therefore allows us to measure the realistic performance of TOD systems in a varied set of the world's languages.We use MULTI 3 NLU ++ to benchmark state-of-the-art multilingual models for the NLU tasks of intent detection and slot labelling for TOD systems in the multilingual setting.The results demonstrate the challenging nature of the dataset, particularly in the low-resource language setting, offering ample room for future experimentation in multi-domain multilingual TOD setups.
Modular Visual Question Answering via Code Generation

We present a framework that formulates visual question answering as modular code generation.In contrast to prior work on modular approaches to VQA, our approach requires no additional training and relies on pre-trained language models (LMs), visual models pre-trained on image-caption pairs, and fifty VQA examples used for in-context learning.The generated Python programs invoke and compose the outputs of the visual models using arithmetic and conditional logic.Our approach improves accuracy on the COVR dataset by at least 3% and on the GQA dataset by 2% compared to the few-shot baseline that does not employ code generation.
Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval

Contrastive learning has been successfully used for retrieval of semantically aligned sentences, but it often requires large batch sizes and carefully engineered heuristics to work well.In this paper, we instead propose a generative model for learning multilingual text embeddings which can be used to retrieve or score sentence pairs.Our model operates on parallel data in N languages and, through an approximation we introduce, efficiently encourages source separation in this multilingual setting, separating semantic information that is shared between translations from stylistic or language-specific variation.We show careful large-scale comparisons between contrastive and generation-based approaches for learning multilingual text embeddings, a comparison that has not been done to the best of our knowledge despite the popularity of these approaches.We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval-the last of which we introduce in this paper.Overall, our Variational Multilingual Source-Separation Transformer (VMSST) model outperforms both a strong contrastive and generative baseline on these tasks. 1
Multi-Document Summarization with Centroid-Based Pretraining

In Multi-Document Summarization (MDS), the input can be modeled as a set of documents, and the output is its summary.In this paper, we focus on pretraining objectives for MDS.Specifically, we introduce a novel pretraining objective, which involves selecting the ROUGEbased centroid of each document cluster as a proxy for its summary.Our objective thus does not require human written summaries and can be utilized for pretraining on a dataset consisting solely of document sets.Through zero-shot, few-shot, and fully supervised experiments on multiple MDS datasets, we show that our model Centrum is better or comparable to a state-ofthe-art model.We make the pretrained and finetuned models freely available to the research community 1 .
Reinforcement Learning for Topic Models

We apply reinforcement learning techniques to topic modeling by replacing the variational autoencoder in ProdLDA with a continuous action space reinforcement learning policy.We train the system with a policy gradient algorithm RE-INFORCE.Additionally, we introduced several modifications: modernize the neural network architecture, weight the ELBO loss, use contextual embeddings, and monitor the learning process via computing topic diversity and coherence for each training step.Experiments are performed on 11 data sets.Our unsupervised model outperforms all other unsupervised models and performs on par with or better than most models using supervised labeling.Our model is outperformed on certain data sets by a model using supervised labeling and contrastive learning.We have also conducted an ablation study to provide empirical evidence of performance improvements from changes we made to ProdLDA and found that the reinforcement learning formulation boosts performance.We open-source our code implementation 1 .
Putting Natural in Natural Language Processing

Human language is firstly spoken and only secondarily written.Text, however, is a very convenient and efficient representation of language, and modern civilization has made it ubiquitous.Thus the field of NLP has overwhelmingly focused on processing written rather than spoken language.Work on spoken language, on the other hand, has been siloed off within the largely separate speech processing community which has been inordinately preoccupied with transcribing speech into text.Recent advances in deep learning have led to a fortuitous convergence in methods between speech processing and mainstream NLP.Arguably, the time is ripe for a unification of these two fields, and for starting to take spoken language seriously as the primary mode of human communication.Truly natural language processing could lead to better integration with the rest of language science and could lead to systems which are more data-efficient and more human-like, and which can communicate beyond the textual modality.
Zero-Shot and Few-Shot Stance Detection on Varied Topics via Conditional Generation

Zero-shot and few-shot stance detection identify the polarity of text with regard to a certain target when we have only limited or no training resources for the target.Previous work generally formulates the problem into a classification setting, ignoring the potential use of label text.In this paper, we instead utilize a conditional generation framework and formulate the problem as denoising from partially-filled templates, which can better utilize the semantics among input, label, and target texts.We further propose to jointly train an auxiliary task, target prediction, and to incorporate manually constructed incorrect samples with unlikelihood training to improve the representations for both target and label texts.We also verify the effectiveness of target-related Wikipedia knowledge with the generation framework.Experiments show that our proposed method significantly outperforms several strong baselines on VAST, and achieves new state-of-the-art performance. 1
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank

Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications.Recently, contrastive learning has been widely adopted which derives highquality sentence representations by pulling similar semantics closer and pushing dissimilar ones away.However, these methods fail to capture the fine-grained ranking information among the sentences, where each sentence is only treated as either positive or negative.In many real-world scenarios, one needs to distinguish and rank the sentences based on their similarities to a query sentence, e.g., very relevant, moderate relevant, less relevant, irrelevant, etc.In this paper, we propose a novel approach, RankCSE, for unsupervised sentence representation learning, which incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework.In particular, we learn semantically discriminative sentence representations by simultaneously ensuring ranking consistency between two representations with different dropout masks, and distilling listwise ranking knowledge from the teacher.An extensive set of experiments are conducted on both semantic textual similarity (STS) and transfer (TR) tasks.Experimental results demonstrate the superior performance of our approach over several state-of-the-art baselines.
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

While large language models (LLMs), such as GPT-3, appear to be robust and general, their reasoning ability is not at a level to compete with the best models trained for specific natural language reasoning problems.In this study, we observe that a large language model can serve as a highly effective few-shot semantic parser.It can convert natural language sentences into a logical form that serves as input for answer set programs, a logic-based declarative knowledge representation formalism.The combination results in a robust and general system that can handle multiple question-answering tasks without requiring retraining for each new task.It only needs a few examples to guide the LLM's adaptation to a specific task, along with reusable ASP knowledge modules that can be applied to multiple tasks.We demonstrate that this method achieves state-of-the-art performance on several NLP benchmarks, including bAbI, StepGame, CLUTRR, and gSCAN.Additionally, it successfully tackles robot planning tasks that an LLM alone fails to solve.
LENS: A Learnable Evaluation Metric for Text Simplification

Training learnable metrics using modern language models has recently emerged as a promising method for the automatic evaluation of machine translation.However, existing human evaluation datasets for text simplification have limited annotations that are based on unitary or outdated models, making them unsuitable for this approach.To address these issues, we introduce the SIMPEVAL corpus that contains: SIMPEVAL PAST , comprising 12K human ratings on 2.4K simplifications of 24 past systems, and SIMPEVAL 2022 , a challenging simplification benchmark consisting of over 1K human ratings of 360 simplifications including GPT-3.5 generated text.Training on SIMPEVAL, we present LENS, a Learnable Evaluation Metric for Text Simplification.Extensive empirical results show that LENS correlates much better with human judgment than existing metrics, paving the way for future progress in the evaluation of text simplification.We also introduce RANK & RATE, a human evaluation framework that rates simplifications from several models in a list-wise manner using an interactive interface, which ensures both consistency and accuracy in the evaluation process and is used to create the SIMPEVAL datasets.* Equal contribution.
Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale

Language models have been shown to perform better with an increase in scale on a wide variety of tasks via the in-context learning paradigm.In this paper, we investigate the hypothesis that the ability of a large language model to in-context learn-perform a task is not uniformly spread across all of its underlying components.Using a 66 billion parameter language model (OPT-66B) across a diverse set of 14 downstream tasks, we find this is indeed the case: ∼70% of the attention heads and ∼20% of the feed forward networks can be removed with minimal decline in task performance.We find substantial overlap in the set of attention heads (un)important for incontext learning across tasks and number of in-context examples.We also address our hypothesis through a task-agnostic lens, finding that a small set of attention heads in OPT-66B score highly on their ability to perform primitive induction operations associated with incontext learning, namely, prefix matching and copying.These induction heads overlap with task-specific important heads, reinforcing arguments by Olsson et al. (2022) regarding induction head generality to more sophisticated behaviors associated with in-context learning.Overall, our study provides several insights that indicate large language models may be undertrained for in-context learning and opens up questions on how to pre-train language models to more effectively perform in-context learning.
Enhancing Educational Dialogues: A Reinforcement Learning Approach for Generating AI Teacher Responses

Reinforcement Learning remains an underutilized method of training and fine-tuning Language Models (LMs) despite recent successes.This paper presents a simple approach of finetuning a language model with Reinforcement Learning to achieve competitive performance on the BEA 2023 Shared Task whose goal is to automatically generate teacher responses in educational dialogues.We utilized the novel NLPO algorithm that masks out tokens during generation to direct the model towards generations that maximize a reward function.We show results for both the t5-base model with 220 million parameters from the HuggingFace repository submitted to the leaderboard that, despite its comparatively small size, has achieved a good performance on both test and dev set, as well as GPT-2 with 124 million parameters.The presented results show that despite maximizing only one of the metrics used in the evaluation as a reward function our model scores highly in the other metrics as well.
SLDT: Sequential Latent Document Transformer for Multilingual Document-based Dialogue

Multilingual document-grounded dialogue, where the system is required to generate responses based on both the conversation multilingual context and external knowledge sources.Traditional pipeline methods for knowledge identification and response generation, while effective in certain scenarios, suffer from error propagation issues and fail to capture the interdependence between these two sub-tasks.To overcome these challenges, we propose the application of the SLDT method, which treats passage-knowledge selection as a sequential decision process rather than a single-step decision process.We achieved the winner 3rd in dialdoc 2023 and we also validated the effectiveness of our method on other datasets.The ablation experiment also shows that our method significantly improves the basic model compared to other methods.
Improving Automatic Quotation Attribution in Literary Novels

Current models for quotation attribution in literary novels assume varying levels of available information in their training and test data, which poses a challenge for in-the-wild inference.Here, we approach quotation attribution as a set of four interconnected sub-tasks: character identification, coreference resolution, quotation identification, and speaker attribution.We benchmark state-of-the-art models on each of these sub-tasks independently, using a large dataset of annotated coreferences and quotations in literary novels (the Project Dialogism Novel Corpus).We also train and evaluate models for the speaker attribution task in particular, showing that a simple sequential prediction model achieves accuracy scores on par with state-of-the-art models 1 .
PromptNER: Prompt Locating and Typing for Named Entity Recognition

Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks.To adopt prompt learning in the NER task, two kinds of methods have been explored from a pair of symmetric perspectives, populating the template by enumerating spans to predict their entity types or constructing type-specific prompts to locate entities.However, these methods not only require a multi-round prompting manner with a high time overhead and computational cost, but also require elaborate prompt templates, that are difficult to apply in practical scenarios.In this paper, we unify entity locating and entity typing into prompt learning, and design a dual-slot multi-prompt template with the position slot and type slot to prompt locating and typing respectively.Multiple prompts can be input to the model simultaneously, and then the model extracts all entities by parallel predictions on the slots.To assign labels for the slots during training, we design a dynamic template filling mechanism that uses the extended bipartite graph matching between prompts and the ground-truth entities.We conduct experiments in various settings, including resource-rich flat and nested NER datasets and low-resource indomain and cross-domain datasets.Experimental results show that the proposed model achieves a significant performance improvement, especially in the cross-domain few-shot setting, which outperforms the state-of-the-art model by +7.7% on average 1 .
Human Inspired Progressive Alignment and Comparative Learning for Grounded Word Acquisition

Human language acquisition is an efficient, supervised, and continual process.In this work, we took inspiration from how human babies acquire their first language, and developed a computational process for word acquisition through comparative learning.Motivated by cognitive findings, we generated a small dataset that enables the computation models to compare the similarities and differences of various attributes, learn to filter out and extract the common information for each shared linguistic label.We frame the acquisition of words as not only the information filtration process, but also as representation-symbol mapping.This procedure does not involve a fixed vocabulary size, nor a discriminative objective, and allows the models to continually learn more concepts efficiently.Our results in controlled experiments have shown the potential of this approach for efficient continual learning of grounded words.
An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language Models

The increasingly large size of modern pretrained language models not only makes them inherit more human-like biases from the training corpora, but also makes it computationally expensive to mitigate such biases.In this paper, we investigate recent parameter-efficient methods in combination with counterfactual data augmentation (CDA) for bias mitigation.We conduct extensive experiments with prefix tuning, prompt tuning, and adapter tuning on different language models and bias types to evaluate their debiasing performance and abilities to preserve the internal knowledge of a pre-trained model.We find that the parameter-efficient methods (i) are effective in mitigating gender bias, where adapter tuning is consistently the most effective one and prompt tuning is more suitable for GPT-2 than BERT, (ii) are less effective when it comes to racial and religious bias, which may be attributed to the limitations of CDA, and (iii) can perform similarly to or sometimes better than full fine-tuning with improved time and memory efficiency, as well as maintain the internal knowledge in BERT and GPT-2, evaluated via fact retrieval and downstream fine-tuning.
Improving Continual Relation Extraction by Distinguishing Analogous Semantics

Continual relation extraction (RE) aims to learn constantly emerging relations while avoiding forgetting the learned relations.Existing works store a small number of typical samples to re-train the model for alleviating forgetting.However, repeatedly replaying these samples may cause the overfitting problem.We conduct an empirical study on existing works and observe that their performance is severely affected by analogous relations.To address this issue, we propose a novel continual extraction model for analogous relations.Specifically, we design memory-insensitive relation prototypes and memory augmentation to overcome the overfitting problem.We also introduce integrated training and focal knowledge distillation to enhance the performance on analogous relations.Experimental results show the superiority of our model and demonstrate its effectiveness in distinguishing analogous relations and overcoming overfitting.