Canada

We propose a novel task-agnostic in-domain pre-training method that sits between generic pre-training and fine-tuning. Our approach selectively masks in-domain keywords, i.e., words that provide a compact representation of the target domain. We identify such keywords using KeyBERT (Grootendorst, 2020). We evaluate our approach using six different settings: three datasets combined with two distinct pre-trained language models (PLMs). Our results reveal that the fine-tuned PLMs adapted using our in-domain pre-training strategy outperform PLMs that used in-domain pre-training with random masking as well as those that followed the common pre-train-then-fine-tune paradigm. Further, the overhead of identifying in-domain keywords is reasonable, e.g., 7-15% of the pre-training time (for two epochs) for BERT Large (Devlin et al., 2019).

ACL 2023

Do not Mask Randomly: Effective Domain-adaptive Pre-training by Masking In-domain Keywords

selective masking pre-training

domain-adaptive pre-training

in-domain pre-training

fine-tuning

domain adaptation

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

We present Relational Sentence Embedding (RSE), a new paradigm to further discover the potential of sentence embeddings. Prior work mainly models the similarity between sentences based on their embedding distance. Because of the complex semantic meanings conveyed, sentence pairs can have various relation types, including but not limited to entailment, paraphrasing, and question-answer. It poses challenges to existing embedding methods to capture such relational information. We handle the problem by learning associated relational embeddings. Specifically, a relation-wise translation operation is applied to the source sentence to infer the corresponding target sentence with a pre-trained Siamese-based encoder. The fine-grained relational similarity scores can be computed from learned embeddings. We benchmark our method on 19 datasets covering a wide range of tasks, including semantic textual similarity, transfer, and domain-specific tasks. Experimental results show that our method is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art sentence embedding methods.

Relational Sentence Embedding for Flexible Semantic Matching

Temporal Knowledge Graph Completion aims to complete missing entities or relations under temporal constraints. Previous tensor decomposition-based models for TKGC only independently consider the combination of one single relation with one single timestamp, ignoring the global nature of the embedding. We propose a Frequency Attention (FA) model to capture the global temporal dependencies between one relation and the entire timestamp. Specifically, we use Discrete Cosine Transform (DCT) to capture the frequency of the timestamp embedding and further compute the frequency attention weight to scale embedding. Meanwhile, the previous temporal tucker decomposition method uses a simple norm regularization to constrain the core tensor, which limits the optimization performance. Thus, we propose Orthogonal Regularization (OR) variants for the core tensor, which can limit the non-superdiagonal elements of the 3-rd core tensor. Experiments on three standard TKGC datasets demonstrate that our method outperforms the state-of-the-art results on several metrics. The results suggest that the direct-current component is not the best feature for TKG representation learning. Additional analysis shows the effectiveness of our FA and OR models, even with smaller embedding dimensions.

Tucker Decomposition with Frequency Attention for Temporal Knowledge Graph Completion

Image captioning typically involves an image encoder to extract meaningful image features, and a text decoder to generate appropriate sentences. Powerful pretrained models can be used for both image encoding and text decoding; but in this case, a separate multimodal translation stage between image-encoder output features and text-decoder input features must be learned. 
One exception is when image and text features are already aligned by construction, as in the CLIP model (Contrastive Language and Image Pretraining -- a bimodal network pretrained on 400M image-text pairs). Pretrained CLIP-image features can be directly fed to a text-decoder trained to reconstruct captions from their pretrained CLIP-text features. Here we show that this direct captioning method is in fact sub-optimal. 
Instead, we propose an alternative method to translate CLIP-image features into CLIP-text features in a strictly unsupervised way, using the CycleGAN architecture -- originally designed for unpaired image-to-image translation. Our Latent CycleGAN, optimized solely for an unsupervised cycle-consistency objective, generates CLIP-text latent features conditioned on CLIP-image latent features and vice-versa. Using these CLIP-text latent features as input to the text decoder, our method largely outperforms the direct captioning method that uses CLIP-image features -- despite the fact that CLIP’s large-scale pretraining should have already aligned the two feature spaces. This implies that cycle-consistency on unmatched multimodal data can be efficiently implemented in a bimodal latent space, and that CLIP-based image captioning can be improved without additional supervised training.

CLIP-based image captioning via unsupervised cycle-consistency in the latent space

Sequence-to-sequence (seq2seq) models have been widely used for natural language processing, computer vision, and other deep learning tasks. We find that seq2seq models trained with early-stopping suffer from issues at the token level. In particular, while some tokens in the vocabulary demonstrate overfitting, others underfit when training is stopped. Experiments show that the phenomena are pervasive in different models, even in fine-tuned large pretrained-models. We identify three major factors that influence token-level fitting, which include token frequency, parts-of-speech, and prediction discrepancy. Further, we find that external factors such as language, model size, domain, data scale, and pretraining can also influence the fitting of tokens.

Token-level Fitting Issues of Seq2seq Models

Exemplification modeling is a recently proposed task that aims to produce a viable sentence using a target word that takes on a specific meaning. This task can be particularly challenging for polysemous words since they can have multiple meanings. In this paper, we propose a one-shot variant of the exemplification modeling task such that labeled data is not needed during training, making it possible to train our system using a raw text corpus. Given one example at test time, our proposed approach can generate diverse and fluent examples where the target word accurately matches its intended meaning. We compare our approach to a fully-supervised baseline trained with different amounts of data and focus our evaluation on polysemous words. We use both automatic and human evaluations to demonstrate how each model performs on both seen and unseen words. Our proposed approach performs similarly to the fully-supervised baseline despite not using labeled data during training.

One-Shot Exemplification Modeling via Latent Sense Representations

Few-shot learning and meta-learning have attracted increasing attention in recent years. Meta-learning in NLP has also shown great progress, including subset masked language modeling tasks (SMLMT). Although SMLMT has led to improved few-shot generalization, its potential for further uses has not been explored. In this paper, we propose SentiSMLMT, an extension of SMLMT which injects sentiment knowledge into the model by utilizing sentiment lexicon within the self-supervised framework. Experimental results show that our approach is simple but effective, achieving significant improvements in sentiment-related tasks.

Enhancing Sentiment Knowledge via Self-Supervised Meta-Learning

Sentence embedding is one of the most fundamental tasks in Natural Language Processing and plays an important role in various tasks.
The recent breakthrough in sentence embedding is achieved by pre-trained language models (PLMs). 
Despite its success, an embedded vector (Sen2Vec) representing a point estimate does not naturally
express uncertainty in a task-agnostic way.
This paper thereby proposes an efficient framework on probabilistic sentence embedding (Sen2Pro) from PLMs, and it represents a sentence as a probability density distribution in an embedding space to reflect both model uncertainty and data uncertainty (i.e., many-to-one nature) in the sentence representation. 
The proposed framework performs in a plug-and-play way without retraining PLMs anymore, and it is easy to implement and generally applied on top of any PLM. 
The superiority of Sen2Pro over Sen2Vec has been theoretically verified and practically illustrated on different NLP tasks.

Sen2Pro: A Probabilistic Perspective to Sentence Embedding from Pre-trained Language Model

Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP), partly owing to the massive amounts of world knowledge they memorize during pretraining.
While many downstream applications provide the model with an informational context to aid its underlying task, how the model’s world knowledge interacts with the factual information presented in the context remains under explored. As a desirable behavior, an LLM should give precedence to the context whenever it contains task-relevant information that conflicts with the model's memorized knowledge. 
This enables model predictions to be grounded in the context, which then facilitates updating specific model predictions without frequently retraining the model. By contrast, when the context is irrelevant to the task, the model should ignore it and fall back on its internal knowledge. In this paper, we undertake a first joint study of the aforementioned two properties, namely \textit{controllability} and \textit{robustness}, in the context of LLMs. We demonstrate that state-of-the-art T5 and PaLM models (both pretrained and finetuned) could exhibit low controllability and robustness that does not improve with increasing the model size. As a solution, we propose a simple yet effective method – \textbf{k}nowledge \textbf{a}ware \textbf{f}ine\textbf{t}uning (KAFT) – to strengthen both controllability and robustness by injecting counterfactual and irrelevant contexts to standard supervised datasets. Our comprehensive evaluation showcases the utility of KAFT across model architectures and sizes.

Large Language Models with Controllable Working Memory

Recent developments of dense retrieval rely on quality representations of queries and contexts from pre-trained query and context encoders. In this paper, we introduce TOUR (Test-Time Optimization of Query Representations), which further optimizes instance-level query representations guided by signals from test-time retrieval results. We leverage a cross-encoder re-ranker to provide fine-grained pseudo labels over retrieval results and iteratively optimize query representations with gradient descent. Our theoretical analysis reveals that TOUR can be viewed as a generalization of the classical Rocchio algorithm for pseudo relevance feedback, and we present two variants that leverage pseudo-labels as hard binary or soft continuous labels. We first apply TOUR on phrase retrieval with our proposed phrase re-ranker, and also evaluate its effectiveness on passage retrieval with an off-the-shelf re-ranker. TOUR greatly improves end-to-end open-domain question answering accuracy, as well as passage retrieval performance. TOUR also consistently improves direct re-ranking by up to 2.0% while running 1.3-2.4x faster with an efficient implementation.

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