Canada

Machine comprehension of procedural texts is essential for reasoning about the steps and automating the procedures. However, this requires identifying entities within a text and resolving the relationships between the entities. Previous work focused on the cooking domain and proposed a framework to convert a recipe text into a flow graph (FG) representation. In this work, we propose a framework based on the recipe FG for flow graph prediction of open-domain procedural texts. To investigate flow graph prediction performance in non-cooking domains, we introduce the wikiHow-FG corpus from articles on wikiHow, a website of how-to instruction articles. In experiments, we consider using the existing recipe corpus and performing domain adaptation from the cooking to the target domain. Experimental results show that the domain adaptation models achieve higher performance than those trained only on the cooking or target domain data.

ACL 2023

Towards Flow Graph Prediction of Open-Domain Procedural Texts

flow graph

procedural text

deep learning

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
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you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
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enormous contributions to the conference over the past year.

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##### **[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.

The widespread usage of latent language representations via pre-trained language models (LMs) suggests that they are a promising source of structured knowledge. However, existing methods focus only on a single object per subject-relation pair, even though often multiple objects are correct. To overcome this limitation, we analyze these representations for their potential to yield materialized multi-object relational knowledge. We formulate the problem as a rank-then-select task. For ranking candidate objects, we evaluate existing prompting techniques and propose new ones incorporating domain knowledge. Among the selection methods, we find that choosing objects with a likelihood above a learned relation-specific threshold gives a 49.5% F1 score. Our results highlight the difficulty of employing LMs for the multi-valued slot-filling task and pave the way for further research on extracting relational knowledge from latent language representations.

Extracting Multi-valued Relations from Language Models

Recent models have shown that incorporating syntactic knowledge into the semantic role labelling (SRL) task leads to a significant improvement. In this paper, we propose Syntax-aware Graph-to-Graph Transformer (SynG2G-Tr) model, which encodes the syntactic structure using a novel way to input graph relations as embeddings, directly into the self-attention mechanism of Transformer. This approach adds a soft bias towards attention patterns that follow the syntactic structure but also allows the model to use this information to learn alternative patterns. We evaluate our model on both span-based and dependency-based SRL datasets, and outperform previous alternative methods in both in-domain and out-of-domain settings, on CoNLL 2005 and CoNLL 2009 datasets.

Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling

Recent advances in prompt tuning have proven effective as a new language modeling paradigm for various natural language understanding tasks. However, it is challenging to adapt the soft prompt embeddings to different domains or generalize to low-data settings when learn- ing soft prompts itself is unstable, task-specific, and bias-prone. This paper proposes a prin- cipled learning framework—soft prompt con- struction (SPC)—to facilitate learning domain- adaptable soft prompts. Derived from the SPC framework is a simple loss that can plug into various models and tuning approaches to im- prove their cross-domain performance. We show SPC can improve upon SOTA for con- textual query rewriting, summarization, and paraphrase detection by up to 5%, 19%, and 16%, respectively.

SPC: Soft Prompt Construction for Cross Domain Generalization

When humans learn a new word, they can infer its grammatical properties from very few examples and apply this knowledge across grammatical contexts. Drawing inspiration from psycholinguistics, we conduct word-learning experiments to assess whether Long Short-Term Memory (LSTM) networks can achieve a similar level of grammatical generalisation. Although Transformer-based language models are successful few-shot learners, their complexity, size, and large data requirements render them less suitable for direct comparison with human language learning. Here, we test whether an LSTM, trained on a French corpus, can learn the grammatical gender of a previously unseen noun from a few examples in one agreement context and apply it to another. Our extensive experiments show that the LSTM effectively generalises grammatical knowledge. For example, the model learns the gender of a novel noun from a single example using gender-marked articles, 'le(m)' or 'la(f)', and applies this knowledge in a different context to predict noun-adjective and noun-participle gender agreement. This suggests that LSTMs do represent grammatical gender as a context-invariant property, similar to humans. Additionally, evaluating the biases and mechanisms that emerge from few-shot learning provides testable hypotheses for cognitive studies of human learning in relation to language models.

LSTMs Acquire More Than One Aspect of Gender with Few-shot Learning

Speech language pathologists rely on information spanning the layers of language, often drawing from multiple layers (e.g. phonology & semantics) at once. Recent innovations in large language models (LLMs) have been shown to build powerful representations for many complex language structures, especially syntax and semantics, unlocking the potential of large datasets through self-supervised learning techniques. However, these datasets are overwhelmingly orthographic, favoring writing systems like the English alphabet, a natural but phonetically imprecise choice. Meanwhile, LLM support for the international phonetic alphabet (IPA) ranges from poor to absent. Further, LLMs encode text at a word- or near-word level, and pre-training tasks have little to gain from phonetic/phonemic representations. In this paper, we introduce BORT, an LLM for mixed orthography/IPA meant to overcome these limitations. To this end, we extend the pre-training of an existing LLM with our own self-supervised pronunciation tasks. We then fine-tune for a clinical task that requires simultaneous phonological and semantic analysis. For an “easy” and “hard” version of these tasks, we show that fine-tuning from our models is more accurate by a relative 24% and 29%, and improved on character error rates by a relative 75% and 31%, respectively, than those starting from the original model.

Mixed Orthographic/Phonemic Language Modeling: Beyond Orthographically Restricted Transformers (BORT)

Although Question Answering (QA) have advanced to the human-level language skills in NLP tasks, there is still a problem: the QA model gets confused when there are similar sentences or paragraphs. Existing studies focus on enhancing the text understanding of the candidate answers to improve the overall performance of the QA models. However, since these methods focus on re-ranking queries or candidate answers, they fail to resolve the confusion when many generated answers are similar to the expected answer. To address these issues, we propose a novel contrastive learning framework called ContrastiveQA that alleviates the confusion problem in answer extraction. We propose a supervised method where we generate positive and negative samples from the candidate answers and the given answer, respectively. 
We thus introduce ContrastiveQA, which uses contrastive learning with sampling data to reduce incorrect answers. 
Experimental results on four QA benchmarks show the effectiveness of the proposed method.

Enhancing text comprehension for Question Answering with Contrastive Learning


Adversarial evaluations of language models typically focus on English alone. In this paper, we performed a multilingual evaluation of Named Entity Recognition (NER) in terms of its robustness to small perturbations in the input. Our results showed the NER models we explored across three languages (English, German and Hindi) are not very robust to such changes, as indicated by the fluctuations in the overall F1 score as well as in a more fine-grained evaluation. With that knowledge, we further explored whether it is possible to improve the existing NER models using a part of the generated adversarial data sets as augmented training data to train a new NER model or as fine-tuning data to adapt an existing NER model. Our results showed that both these approaches improve performance on the original as well as adversarial test sets. While there is no significant difference between the two approaches for English, re-training is significantly better than fine-tuning for German and Hindi.

A Multilingual Evaluation of NER Robustness to Adversarial Inputs

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).

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

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.

Towards Flow Graph Prediction of Open-Domain Procedural Texts

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Extracting Multi-valued Relations from Language Models

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