ymoslem/acl-abstracts huggingface dataset:
Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method (2023)

The large scale of pre-trained language models poses a challenge for their deployment on various devices, with a growing emphasis on methods to compress these models, particularly knowledge distillation. However, current knowledge distillation methods rely on the model`s intermediate layer features and the golden labels (also called hard labels), which usually require aligned model architecture and enough labeled data respectively. Moreover, the parameters of vocabulary are usually neglected in existing methods. To address these problems, we propose a general language model distillation (GLMD) method that performs two-stage word prediction distillation and vocabulary compression, which is simple and surprisingly shows extremely strong performance. Specifically, GLMD supports more general application scenarios by eliminating the constraints of dimension and structure between models and the need for labeled datasets through the absence of intermediate layers and golden labels. Meanwhile, based on the long-tailed distribution of word frequencies in the data, GLMD designs a strategy of vocabulary compression through decreasing vocabulary size instead of dimensionality. Experimental results show that our method outperforms 25 state-of-the-art methods on the SuperGLUE benchmark, achieving an average score that surpasses the best method by 3{\%}.
Diable: Efficient Dialogue State Tracking as Operations on Tables (2023)

Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn. This approach is inefficient, especially when the number of slots is large and the conversation is long. We propose Diable, a new task formalisation that simplifies the design and implementation of efficient DST systems and allows one to easily plug and play large language models. We represent the dialogue state as a table and formalise DST as a table manipulation task. At each turn, the system updates the previous state by generating table operations based on the dialogue context. Extensive experimentation on the MultiWoz datasets demonstrates that Diable (i) outperforms strong efficient DST baselines, (ii) is 2.4x more time efficient than current state-of-the-art methods while retaining competitive Joint Goal Accuracy, and (iii) is robust to noisy data annotations due to the table operations approach.
Neural Topic Modeling based on Cycle Adversarial Training and Contrastive Learning (2023)

Neural topic models have been widely used to extract common topics across documents. Recently, contrastive learning has been applied to variational autoencoder-based neural topic models, achieving promising results. However, due to the limitation of the unidirectional structure of the variational autoencoder, the encoder is enhanced with the contrastive loss instead of the decoder, leading to a gap between model training and evaluation. To address the limitation, we propose a novel neural topic modeling framework based on cycle adversarial training and contrastive learning to apply contrastive learning on the generator directly. Specifically, a self-supervised contrastive loss is proposed to make the generator capture similar topic information, which leads to better topic-word distributions. Meanwhile, a discriminative contrastive loss is proposed to cooperate with the self-supervised contrastive loss to balance the generation and discrimination. Moreover, based on the reconstruction ability of the cycle generative adversarial network, a novel data augmentation strategy is designed and applied to the topic distribution directly. Experiments have been conducted on four benchmark datasets and results show that the proposed approach outperforms competitive baselines.
Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive Summarization (2023)

Most Transformer based abstractive summarization systems have a severe mismatch between training and inference, i.e., exposure bias. From diverse perspectives, we introduce a simple multi-level contrastive learning framework for abstractive summarization (SimMCS) and a tailored sparse decoder self-attention pattern (SDSA) to bridge the gap between training and inference to improve model performance. Compared with previous contrastive objectives focusing only on the relative order of probability mass assigned to non-gold summaries, SimMCS additionally takes their absolute positions into account, which guarantees that the relatively high-quality (positive) summaries among them could be properly assigned high probability mass, and further enhances the capability of discriminating summary quality beyond exploiting potential artifacts of specific metrics. SDSA simulates the possible inference scenarios of deviation in the training phase to get closer to the ideal paradigm. Our approaches outperform the previous state-of-the-art results on two summarization datasets while just adding fairly low overhead. Further empirical analysis shows our model preserves the advantages of prior contrastive methods and possesses strong few-shot learning ability.
Mapping Brains with Language Models: A Survey (2023)

Over the years, many researchers have seemingly made the same observation: Brain and language model activations exhibit some structural similarities, enabling linear partial mappings between features extracted from neural recordings and computational language models. In an attempt to evaluate how much evidence has been accumulated for this observation, we survey over 30 studies spanning 10 datasets and 8 metrics. How much evidence has been accumulated, and what, if anything, is missing before we can draw conclusions? Our analysis of the evaluation methods used in the literature reveals that some of the metrics are less conservative. We also find that the accumulated evidence, for now, remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism.
Parameter-Efficient Finetuning for Robust Continual Multilingual Learning (2023)

We introduce and study the problem of Continual Multilingual Learning (CML) where a previously trained multilingual model is periodically updated using new data arriving in stages. If the new data is present only in a subset of languages, we find that the resulting model shows improved performance only on the languages included in the latest update (and a few closely related languages) while its performance on all the remaining languages degrade significantly. We address this challenge by proposing LAFT-URIEL, a parameter-efficient finetuning strategy which aims to increase the number of languages on which the model improves after an update, while reducing the magnitude of loss in performance for the remaining languages. LAFT-URIEL uses linguistic knowledge to balance overfitting and knowledge sharing across languages, allowing for an additional 25{\%} of task languages to see an improvement in performance after an update, while also reducing the average magnitude of losses on the remaining languages by 78{\%} relative.
Interpretable Multimodal Misinformation Detection with Logic Reasoning (2023)

Multimodal misinformation on online social platforms is becoming a critical concern due to increasing credibility and easier dissemination brought by multimedia content, compared to traditional text-only information. While existing multimodal detection approaches have achieved high performance, the lack of interpretability hinders these systems' reliability and practical deployment. Inspired by Neural-Symbolic AI which combines the learning ability of neural networks with the explainability of symbolic learning, we propose a novel logic-based neural model for multimodal misinformation detection which integrates interpretable logic clauses to express the reasoning process of the target task. To make learning effective, we parameterize the symbolic logical elements using neural representations, which facilitate the automatic generation and evaluation of meaningful logic clauses. Additionally, to make our framework generalizable across diverse misinformation sources, we introduce five meta-predicates that can be instantiated with different correlations. Results on three public datasets (Twitter, Weibo, and Sarcasm) demonstrate the feasibility and versatility of our model.
Semantic-conditioned Dual Adaptation for Cross-domain Query-based Visual Segmentation (2023)

Visual segmentation from language queries has attracted significant research interest. Despite the effectiveness, existing works require expensive labeling and suffer severe degradation when deployed to an unseen domain. In this paper, we investigate a novel task Cross-domain Query-based Visual Segmentation (CQVS), aiming to adapt the segmentation model from a labeled domain to a new unlabeled domain. The challenges of CQVS stem from three domain discrepancies: (1) multi-modal content shift, (2) uni-modal feature gap and (3) cross-modal relation bias. Existing domain adaptation methods fail to address them comprehensively and precisely (e.g. at pixel level), thus being suboptimal for CQVS. To overcome this limitation, we propose Semantic-conditioned Dual Adaptation (SDA), a novel framework to achieve precise feature- and relation-invariant across domains via a universal semantic structure. The SDA consists of two key components: Content-aware Semantic Modeling (CSM) and Dual Adaptive Branches (DAB). First, CSM introduces a common semantic space across domains to provide uniform guidance. Then, DAB seamlessly leverages this semantic information to develop a contrastive feature branch for category-wise pixel alignment, and design a reciprocal relation branch for relation enhancement via two complementary masks. Extensive experiments on three video benchmarks and three image benchmarks evidence the superiority of our approach over the state-of-the-arts.
Figurative Language Processing: A Linguistically Informed Feature Analysis of the Behavior of Language Models and Humans (2023)

Recent years have witnessed a growing interest in investigating what Transformer-based language models (TLMs) actually learn from the training data. This is especially relevant for complex tasks such as the understanding of non-literal meaning. In this work, we probe the performance of three black-box TLMs and two intrinsically transparent white-box models on figurative language classification of sarcasm, similes, idioms, and metaphors. We conduct two studies on the classification results to provide insights into the inner workings of such models. With our first analysis on feature importance, we identify crucial differences in model behavior. With our second analysis using an online experiment with human participants, we inspect different linguistic characteristics of the four figurative language types.
Taxonomy of Problems in Lexical Semantics (2023)

Semantic tasks are rarely formally defined, and the exact relationship between them is an open question. We introduce a taxonomy that elucidates the connection between several problems in lexical semantics, including monolingual and cross-lingual variants. Our theoretical framework is based on the hypothesis of the equivalence of concept and meaning distinctions. Using algorithmic problem reductions, we demonstrate that all problems in the taxonomy can be reduced to word sense disambiguation (WSD), and that WSD itself can be reduced to some problems, making them theoretically equivalent. In addition, we carry out experiments that strongly support the soundness of the concept-meaning hypothesis, and the correctness of our reductions.
Making Pre-trained Language Models both Task-solvers and Self-calibrators (2023)

Pre-trained language models (PLMs) serve as backbones for various real-world systems. For high-stake applications, it`s equally essential to have reasonable confidence estimations in predictions. While the vanilla confidence scores of PLMs can already be effectively utilized, PLMs consistently become overconfident in their wrong predictions, which is not desirable in practice. Previous work shows that introducing an extra calibration task can mitigate this issue. The basic idea involves acquiring additional data to train models in predicting the confidence of their initial predictions. However, it only demonstrates the feasibility of this kind of method, assuming that there are abundant extra available samples for the introduced calibration task. In this work, we consider the practical scenario that we need to effectively utilize training samples to make PLMs both task-solvers and self-calibrators. Three challenges are presented, including limited training samples, data imbalance, and distribution shifts. We first conduct pilot experiments to quantify various decisive factors in the calibration task. Based on the empirical analysis results, we propose a training algorithm LM-TOAST to tackle the challenges. Experimental results show that LM-TOAST can effectively utilize the training data to make PLMs have reasonable confidence estimations while maintaining the original task performance. Further, we consider three downstream applications, namely selective classification, adversarial defense, and model cascading, to show the practical usefulness of LM-TOAST.
EmbedTextNet: Dimension Reduction with Weighted Reconstruction and Correlation Losses for Efficient Text Embedding (2023)

The size of embeddings generated by large language models can negatively affect system latency and model size in certain downstream practical applications (e.g. KNN search). In this work, we propose EmbedTextNet, a light add-on network that can be appended to an arbitrary language model to generate a compact embedding without requiring any changes in its architecture or training procedure. Specifically, we use a correlation penalty added to the weighted reconstruction loss that better captures the informative features in the text embeddings, which improves the efficiency of the language models. We evaluated EmbedTextNet on three different downstream tasks: text similarity, language modelling, and text retrieval. Empirical results on diverse benchmark datasets demonstrate the effectiveness and superiority of EmbedTextNet compared to state-of-art methodologies in recent works, especially in extremely low dimensional embedding sizes. The developed code for reproducibility is included in the supplementary material.
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing (2023)

Recently, the task of distantly supervised (DS) ultra-fine entity typing has received significant attention. However, DS data is noisy and often suffers from missing or wrong labeling issues resulting in low precision and low recall. This paper proposes a novel ultra-fine entity typing model with denoising capability. Specifically, we build a noise model to estimate the unknown labeling noise distribution over input contexts and noisy type labels. With the noise model, more trustworthy labels can be recovered by subtracting the estimated noise from the input. Furthermore, we propose an entity typing model, which adopts a bi-encoder architecture, is trained on the denoised data. Finally, the noise model and entity typing model are trained iteratively to enhance each other. We conduct extensive experiments on the Ultra-Fine entity typing dataset as well as OntoNotes dataset and demonstrate that our approach significantly outperforms other baseline methods.
INTapt: Information-Theoretic Adversarial Prompt Tuning for Enhanced Non-Native Speech Recognition (2023)

Automatic Speech Recognition (ASR) systems have attained unprecedented performance with large speech models pre-trained based on self-supervised speech representation learning. However, these pre-trained speech models suffer from representational bias as they tend to better represent those prominent accents (i.e., native (L1) English accent) in the pre-training speech corpus than less represented accents, resulting in a deteriorated performance for non-native (L2) English accents. Although there have been some approaches to mitigate this issue, all of these methods require updating the pre-trained model weights. In this paper, we propose Information Theoretic Adversarial Prompt Tuning (INTapt), which introduces prompts concatenated to the original input that can re-modulate the attention of the pre-trained model such that the corresponding input resembles a native (L1) English speech without updating the backbone weights. INTapt is trained simultaneously in the following two manners: (1) adversarial training to reduce accent feature dependence between the original input and the prompt-concatenated input and (2) training to minimize CTC loss for improving ASR performance to a prompt-concatenated input. Experimental results show that INTapt improves the performance of L2 English and increases feature similarity between L2 and L1 accents.
Local Temperature Beam Search: Avoid Neural Text DeGeneration via Enhanced Calibration (2023)

Previous studies have constantly observed that a language model repeats itself, creating repetitions in an output sequence. To cope with the issue, stochastic decoding schemes have been the de facto approaches; the strategies add randomness in inference, hence avoiding the {\textquotedblleft}self-loop{\textquotedblright}. However, the remedy comes at the cost of sacrificing output quality due to the randomness involved. In this work, we introduce a deterministic decoding scheme, local temperature beam search. This inference algorithm is an embarrassingly simple variant of beam search, yet it reduces repetition, whose level is superior to that of a sampling-based decoding algorithm, while maintaining the level of coherence as in beam search. Our idea is rooted in the concept of model calibration; we view a repetition as a casualty from overconfidence in a model. Therefore, our work mitigates the miscalibration present in the course of inference with a post-calibration approach applied in beam-specific manner. Our inference scheme is validated on text completion tasks, in which the repetition problem is seen most clearly, and is exhaustively compared with existing inference schemes.
Explanation Graph Generation via Generative Pre-training over Synthetic Graphs (2023)

The generation of explanation graphs is a significant task that aims to produce explanation graphs in response to user input, revealing the internal reasoning process. This task is challenging due to the significant discrepancy be- tween unstructured user queries and structured explanation graphs. Current research commonly fine-tunes a text-based pre-trained language model on a small downstream dataset that is annotated with labeled graphs. However, due to the limited scale of available datasets, this approach may prove to be insufficient in bridging the gap between natural language text and structured graphs. In this paper, to alleviate the above limitations, we propose a novel pre-trained framework EG3P(for Explanation Graph Generation via Generative Pre-training over synthetic graphs) for the explanation graph generation task. Specifically, we first propose a text-to-graph generative task to pre-train the model with the goal of bridging the text-graph gap. Additionally, we propose an automatic corpus synthesis strategy for synthesizing a large scale of high-quality corpus, reducing the reliance on costly manual annotation methods. Experimental results on ExplaGraphs show the effectiveness of EG3P that our model surpasses all baseline systems with remarkable margins. Besides, further analysis demonstrates that EG3P is able to generate better explanation graphs on actual reasoning tasks such as CommonsenseQA and OpenbookQA.
NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts (2023)

We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains. Previous CGEC research primarily focuses on correcting texts from a single domain, especially learner essays. To broaden the target domain, we annotate multiple references for 12,500 sentences from three native domains, i.e., social media, scientific writing, and examination. We provide solid benchmark results for NaSGEC by employing cutting-edge CGEC models and different training data. We further perform detailed analyses of the connections and gaps between our domains from both empirical and statistical views. We hope this work can inspire future studies on an important but under-explored direction{--}cross-domain GEC.
FORK: A Bite-Sized Test Set for Probing Culinary Cultural Biases in Commonsense Reasoning Models (2023)

It is common sense that one should prefer to eat a salad with a fork rather than with a chainsaw. However, for eating a bowl of rice, the choice between a fork and a pair of chopsticks is culturally relative. We introduce FORK, a small, manually-curated set of CommonsenseQA-style questions for probing cultural biases and assumptions present in commonsense reasoning systems, with a specific focus on food-related customs. We test several CommonsenseQA systems on FORK, and while we see high performance on questions about the US culture, the poor performance of these systems on questions about non-US cultures highlights systematic cultural assumptions aligned with US over non-US cultures.
FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models (2023)

With increasing concerns about data privacy, there is an increasing necessity of fine-tuning pre-trained language models (PLMs) for adapting to downstream tasks located in end-user devices or local clients without transmitting data to the central server. This urgent necessity therefore calls the research of investigating federated learning (FL) for PLMs. However, large PLMs bring the curse of prohibitive communication overhead and local model adaptation costs for the FL system. To this end, we investigate the parameter-efficient tuning (PETuning) of PLMs and develop a corresponding federated benchmark for four representative PETuning methods, dubbed FedPETuning. Specifically, FedPETuning provides the first holistic empirical study of representative PLMs tuning methods in FL, covering privacy attacks, performance comparisons, and resource-constrained analysis. Intensive experimental results have indicated that FedPETuning can efficiently defend against privacy attacks and maintains acceptable performance with reducing heavy resource consumption. The open-source code and data are available at \url{https://github.com/SMILELab-FL/FedPETuning}.