ymoslem/acl-abstracts huggingface dataset:
Hard Sample Aware Prompt-Tuning (2023)

Prompt-tuning based few-shot learning has garnered increasing attention in recent years due to its efficiency and promising capability. To achieve the best performance for NLP tasks with just a few samples, it is vital to include as many informative samples as possible and to avoid misleading ones. However, there is no work in prompt-tuning literature addressing the problem of differentiating informative hard samples from misleading ones in model training, which is challenging due to the lack of supervision signals about the quality of the samples to train a well-performed model. We propose a Hard Sample Aware Prompt-Tuning framework (i.e. HardPT) to solve the non-differentiable problem in hard sample identification with reinforcement learning, and to strengthen the discrimination of the feature space without changing the original data distribution via an adaptive contrastive learning method. An extensive empirical study on a series of NLP tasks demonstrates the capability of HardPT in few-shot scenarios. HardPT obtains new SOTA results on all evaluated NLP tasks, including pushing the SST-5 accuracy to 49.5{\%} (1.1{\%} point absolute improvement), QNLI accuracy to 74.6{\%} (1.9{\%} absolute improvement), NMLI accuracy to 71.5 (0.7{\%} absolute improvement), TACREV $F_1$-score to 28.2 (1.0 absolute improvement), and i2b2/VA $F_1$-score to 41.2 (1.3 absolute improvement).
WikiBio: a Semantic Resource for the Intersectional Analysis of Biographical Events (2023)

Biographical event detection is a relevant task that allows for the exploration and comparison of the ways in which people`s lives are told and represented. This may support several real-life applications in digital humanities and in works aimed at exploring bias about minoritized groups. Despite that, there are no corpora and models specifically designed for this task. In this paper we fill this gap by presenting a new corpus annotated for biographical event detection. The corpus, which includes 20 Wikipedia biographies, was aligned with 5 existing corpora in order to train a model for the biographical event detection task. The model was able to detect all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an F-score of 0.859. Finally, the model was used for performing an analysis of biases about women and non-Western people in Wikipedia biographies.
Best-k Search Algorithm for Neural Text Generation (2023)

Modern natural language generation paradigms require a decoding strategy to obtain quality sequences out of the model. Beam search yields high-quality but low diversity outputs; stochastic approaches suffer from high variance and sometimes low quality. In this work, we propose a deterministic search algorithm balancing both quality and diversity. We first investigate the vanilla best-first search (BFS) algorithm and then propose the best-k search algorithm. Inspired by BFS, we greedily expand the top k nodes, instead of the first node, to boost efficiency and diversity. Upweighting recently discovered nodes accompanied by heap pruning ensures the completeness of the search procedure. Experiments on four NLG tasks show that best-k search yields more diverse and natural outputs compared to strong baselines, while our approach maintains high text quality. The proposed algorithm is parameter-free, lightweight, efficient, and easy-to-use.
Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages (2023)

Building Natural Language Understanding (NLU) capabilities for Indic languages, which have a collective speaker base of more than one billion speakers is absolutely crucial. In this work, we aim to improve the NLU capabilities of Indic languages by making contributions along 3 important axes (i) monolingual corpora (ii) NLU testsets (iii) multilingual LLMs focusing on Indic languages. Specifically, we curate the largest monolingual corpora, IndicCorp, with 20.9B tokens covering 24 languages from 4 language families - a 2.3x increase over prior work, while supporting 12 additional languages. Next, we create a human-supervised benchmark, IndicXTREME, consisting of nine diverse NLU tasks covering 20 languages. Across languages and tasks, IndicXTREME contains a total of 105 evaluation sets, of which 52 are new contributions to the literature. To the best of our knowledge, this is the first effort towards creating a standard benchmark for Indic languages that aims to test the multilingual zero-shot capabilities of pretrained language models. Finally, we train IndicBERT v2, a state-of-the-art model supporting all the languages. Averaged across languages and tasks, the model achieves an absolute improvement of 2 points over a strong baseline. The data and models are available at \url{https://github.com/AI4Bharat/IndicBERT}.
Transforming Visual Scene Graphs to Image Captions (2023)

We propose to TransForm Scene Graphs into more descriptive Captions (TFSGC). In TFSGC, we apply multi-head attention (MHA) to design the Graph Neural Network (GNN) for embedding scene graphs. After embedding, different graph embeddings contain diverse specific knowledge for generating the words with different part-of-speech, e.g., object/attribute embedding is good for generating nouns/adjectives. Motivated by this, we design a Mixture-of-Expert (MOE)-based decoder, where each expert is built on MHA, for discriminating the graph embeddings to generate different kinds of words. Since both the encoder and decoder are built based on the MHA, as a result, we construct a simple and homogeneous encoder-decoder unlike the previous heterogeneous ones which usually apply Fully-Connected-based GNN and LSTM-based decoder. The homogeneous architecture enables us to unify the training configuration of the whole model instead of specifying different training strategies for diverse sub-networks as in the heterogeneous pipeline, which releases the training difficulty. Extensive experiments on the MS-COCO captioning benchmark validate the effectiveness of our TFSGC. The code is in: \url{https://anonymous.4open.science/r/ACL23_TFSGC}.
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks (2023)

Transducer and Attention based Encoder-Decoder (AED) are two widely used frameworks for speech-to-text tasks. They are designed for different purposes and each has its own benefits and drawbacks for speech-to-text tasks. In order to leverage strengths of both modeling methods, we propose a solution by combining Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks. The new method leverages AED`s strength in non-monotonic sequence to sequence learning while retaining Transducer`s streaming property. In the proposed framework, Transducer and AED share the same speech encoder. The predictor in Transducer is replaced by the decoder in the AED model, and the outputs of the decoder are conditioned on the speech inputs instead of outputs from an unconditioned language model. The proposed solution ensures that the model is optimized by covering all possible read/write scenarios and creates a matched environment for streaming applications. We evaluate the proposed approach on the MuST-C dataset and the findings demonstrate that TAED performs significantly better than Transducer for offline automatic speech recognition (ASR) and speech-to-text translation (ST) tasks. In the streaming case, TAED outperforms Transducer in the ASR task and one ST direction while comparable results are achieved in another translation direction.
Improving Domain Generalization for Prompt-Aware Essay Scoring via Disentangled Representation Learning (2023)

Automated Essay Scoring (AES) aims to score essays written in response to specific prompts. Many AES models have been proposed, but most of them are either prompt-specific or prompt-adaptive and cannot generalize well on {\textquotedblleft}unseen{\textquotedblright} prompts. This work focuses on improving the generalization ability of AES models from the perspective of domain generalization, where the data of target prompts cannot be accessed during training. Specifically, we propose a prompt-aware neural AES model to extract comprehensive representation for essay scoring, including both prompt-invariant and prompt-specific features. To improve the generalization of representation, we further propose a novel disentangled representation learning framework. In this framework, a contrastive norm-angular alignment strategy and a counterfactual self-training strategy are designed to disentangle the prompt-invariant information and prompt-specific information in representation. Extensive experimental results on datasets of both ASAP and TOEFL11 demonstrate the effectiveness of our method under the domain generalization setting.
What`s the Meaning of Superhuman Performance in Today`s NLU? (2023)

In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension. These PLMs have achieved impressive results on these benchmarks, even surpassing human performance in some cases. This has led to claims of superhuman capabilities and the provocative idea that certain tasks have been solved. In this position paper, we take a critical look at these claims and ask whether PLMs truly have superhuman abilities and what the current benchmarks are really evaluating. We show that these benchmarks have serious limitations affecting the comparison between humans and PLMs and provide recommendations for fairer and more transparent benchmarks.
PromptNER: Prompt Locating and Typing for Named Entity Recognition (2023)

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 in-domain 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.
Hints on the data for language modeling of synthetic languages with transformers (2023)

Language Models (LM) are becoming more and more useful for providing representations upon which to train Natural Language Processing applications. However, there is now clear evidence that attention-based transformers require a critical amount of language data to produce good enough LMs. The question we have addressed in this paper is to what extent the critical amount of data varies for languages of different morphological typology, in particular those that have a rich inflectional morphology, and whether the tokenization method to preprocess the data can make a difference. These details can be important for low-resourced languages that need to plan the production of datasets. We evaluated intrinsically and extrinsically the differences of five different languages with different pretraining dataset sizes and three different tokenization methods for each. The results confirm that the size of the vocabulary due to morphological characteristics is directly correlated with both the LM perplexity and the performance of two typical downstream tasks such as NER identification and POS labeling. The experiments also provide new evidence that a canonical tokenizer can reduce perplexity by more than a half for a polysynthetic language like Quechua as well as raising F1 from 0.8 to more than 0.9 in both downstream tasks with a LM trained with only 6M tokens.
Neural Machine Translation Methods for Translating Text to Sign Language Glosses (2023)

State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language (SL) glosses. In our experiments, we improve the performance of the transformer-based models via (1) data augmentation, (2) semi-supervised Neural Machine Translation (NMT), (3) transfer learning and (4) multilingual NMT. The proposed methods are implemented progressively on two German SL corpora containing gloss annotations. Multilingual NMT combined with data augmentation appear to be the most successful setting, yielding statistically significant improvements as measured by three automatic metrics (up to over 6 points BLEU), and confirmed via human evaluation. Our best setting outperforms all previous work that report on the same test-set and is also confirmed on a corpus of the American Sign Language (ASL).
Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences? (2023)

Event co-occurrences have been proved effective for event extraction (EE) in previous studies, but have not been considered for event argument extraction (EAE) recently. In this paper, we try to fill this gap between EE research and EAE research, by highlighting the question that \textit{{\textquotedblleft}Can EAE models learn better when being aware of event co-occurrences?{\textquotedblright}}. To answer this question, we reformulate EAE as a problem of table generation and extend a SOTA prompt-based EAE model into a non-autoregressive generation framework, called TabEAE, which is able to extract the arguments of multiple events in parallel. Under this framework, we experiment with 3 different training-inference schemes on 4 datasets (ACE05, RAMS, WikiEvents and MLEE) and discover that via training the model to extract all events in parallel, it can better distinguish the semantic boundary of each event and its ability to extract single event gets substantially improved. Experimental results show that our method achieves new state-of-the-art performance on the 4 datasets. Our code is avilable at \url{https://github.com/Stardust-hyx/TabEAE}.
HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation (2023)

Similes play an imperative role in creative writing such as story and dialogue generation. Proper evaluation metrics are like a beacon guiding the research of simile generation (SG). However, it remains under-explored as to what criteria should be considered, how to quantify each criterion into metrics, and whether the metrics are effective for comprehensive, efficient, and reliable SG evaluation. To address the issues, we establish HAUSER, a holistic and automatic evaluation system for the SG task, which consists of five criteria from three perspectives and automatic metrics for each criterion. Through extensive experiments, we verify that our metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics. Resources of HAUSER are publicly available at \url{https://github.com/Abbey4799/HAUSER}.
Large-scale Lifelong Learning of In-context Instructions and How to Tackle It (2023)

Jointly fine-tuning a Pre-trained Language Model (PLM) on a pre-defined set of tasks with in-context instructions has been proven to improve its generalization performance, allowing us to build a universal language model that can be deployed across task boundaries. In this work, we explore for the first time whether this attractive property of in-context instruction learning can be extended to a scenario in which tasks are fed to the target PLM in a sequential manner. The primary objective of so-called lifelong in-context instruction learning is to improve the target PLM`s instance- and task-level generalization performance as it observes more tasks. DynaInst, the proposed method to lifelong in-context instruction learning, achieves noticeable improvements in both types of generalization, nearly reaching the upper bound performance obtained through joint training.
Controllable Text Generation via Probability Density Estimation in the Latent Space (2023)

Previous work on controllable text generation has explored the idea of control from the latent space, such as optimizing a representation with attribute-specific classifiers or sampling one from relevant discrete samples. However, they cannot effectively model a complex space with diverse attributes, high dimensionality, and asymmetric structure, leaving subsequent controls unsatisfying. In this work, we propose a novel control framework using probability density estimation in the latent space. Our method utilizes an invertible transformation function, the Normalizing Flow, that maps the complex distributions in the latent space to simple Gaussian distributions in the prior space. Thus, we can perform sophisticated and flexible controls in the prior space and feed the control effects back into the latent space owing to the bijection property of invertible transformations. Experiments on single-attribute and multi-attribute control reveal that our method outperforms several strong baselines on attribute relevance and text quality, achieving a new SOTA. Further analysis of control strength adjustment demonstrates the flexibility of our control strategy.
Learning Latent Relations for Temporal Knowledge Graph Reasoning (2023)

Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on historical data. However, due to the limitations in construction tools and data sources, many important associations between entities may be omitted in TKG. We refer to these missing associations as latent relations. Most existing methods have some drawbacks in explicitly capturing intra-time latent relations between co-occurring entities and inter-time latent relations between entities that appear at different times. To tackle these problems, we propose a novel Latent relations Learning method for TKG reasoning, namely L2TKG. Specifically, we first utilize a Structural Encoder (SE) to obtain representations of entities at each timestamp. We then design a Latent Relations Learning (LRL) module to mine and exploit the intra- and inter-time latent relations. Finally, we extract the temporal representations from the output of SE and LRL for entity prediction. Extensive experiments on four datasets demonstrate the effectiveness of L2TKG.
DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function (2023)

Recent advances in neural theorem-proving resort to large language models and tree searches. When proving a theorem, a language model advises single-step actions based on the current proving state and the tree search finds a sequence of correct steps using actions given by the language model. However, prior works often conduct constant computation efforts for each proving state while ignoring that the hard states often need more exploration than easy states. Moreover, they evaluate and guide the proof search solely depending on the current proof state instead of considering the whole proof trajectory as human reasoning does. Here, to accommodate general theorems, we propose a novel Dynamic-Tree Driven Theorem Solver (DT-Solver) by guiding the search procedure with state confidence and proof-level values. Specifically, DT-Solver introduces a dynamic-tree Monte-Carlo search algorithm, which dynamically allocates computing budgets for different state confidences, guided by a new proof-level value function to discover proof states that require substantial exploration. Experiments on two popular theorem-proving datasets, PISA and Mathlib, show significant performance gains by our DT-Solver over the state-of-the-art approaches, with a 6.65{\%} improvement on average in terms of success rate. And especially under low computing resource settings (11.03{\%} improvement on average).
Unsupervised Selective Rationalization with Noise Injection (2023)

A major issue with using deep learning models in sensitive applications is that they provide no explanation for their output. To address this problem, unsupervised selective rationalization produces rationales alongside predictions by chaining two jointly-trained components, a rationale generator and a predictor. Although this architecture guarantees that the prediction relies solely on the rationale, it does not ensure that the rationale contains a plausible explanation for the prediction. We introduce a novel training technique that effectively limits generation of implausible rationales by injecting noise between the generator and the predictor. Furthermore, we propose a new benchmark for evaluating unsupervised selective rationalization models using movie reviews from existing datasets. We achieve sizeable improvements in rationale plausibility and task accuracy over the state-of-the-art across a variety of tasks, including our new benchmark, while maintaining or improving model faithfulness.
Understanding In-Context Learning via Supportive Pretraining Data (2023)

In-context learning (ICL) improves language models' performance on a variety of NLP tasks by simply demonstrating a handful of examples at inference time. It is not well understood why ICL ability emerges, as the model has never been specifically trained on such demonstrations. Unlike prior work that explores implicit mechanisms behind ICL, we study ICL via investigating the pretraining data. Specifically, we first adapt an iterative, gradient-based approach to find a small subset of pretraining data that supports ICL. We observe that a continued pretraining on this small subset significantly improves the model`s ICL ability, by up to 18{\%}. We then compare the supportive subset constrastively with random subsets of pretraining data and discover: (1) The supportive pretraining data to ICL do not have a higher domain relevance to downstream tasks. (2) The supportive pretraining data have a higher mass of rarely occurring, long-tail tokens. (3) The supportive pretraining data are challenging examples where the information gain from long-range context is below average, indicating learning to incorporate difficult long-range context encourages ICL. Our work takes a first step towards understanding ICL via analyzing instance-level pretraining data. Our insights have a potential to enhance the ICL ability of language models by actively guiding the construction of pretraining data in the future.