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How much information do NLP tasks really need from a transformer&#39;s attention mechanism at application-time (inference)?
From recent work, we know that there is sparsity in transformers and that the floating-points within its computation can be discretized to fewer values with minimal loss to task accuracies. 
However, this requires retraining or even creating entirely new models, both of which can be expensive and carbon-emitting. 
Focused on optimizations that do not require training, we systematically study the full range of typical attention values necessary. 
This informs the design of an inference-time quantization technique using both pruning and log-scaled mapping which produces only a few (e.g. $2^3$) unique values. 
Over the tasks of question answering and sentiment analysis, we find nearly 80\% of attention values can be pruned to zeros with minimal ($&lt; 1.0\%$) relative loss in accuracy. 
We use this pruning technique in conjunction with quantizing the attention values to only a 3-bit format, without retraining, resulting in only a 0.8\% accuracy reduction on question answering with fine-tuned RoBERTa.

ACL-IJCNLP 2021

On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers

attention sparsity

efficient transformers

self-attention

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

Registration is now open

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

How much information do NLP tasks really need from a transformer's attention mechanism at application-time (inference)?
From recent work, we know that there is sparsity in transformers and that the floating-points within its computation can be discretized to fewer values with minimal loss to task accuracies. 
However, this requires retraining or even creating entirely new models, both of which can be expensive and carbon-emitting. 
Focused on optimizations that do not require training, we systematically study the full range of typical attention values necessary. 
This informs the design of an inference-time quantization technique using both pruning and log-scaled mapping which produces only a few (e.g. $2^3$) unique values. 
Over the tasks of question answering and sentiment analysis, we find nearly 80\% of attention values can be pruned to zeros with minimal ($< 1.0\%$) relative loss in accuracy. 
We use this pruning technique in conjunction with quantizing the attention values to only a 3-bit format, without retraining, resulting in only a 0.8\% accuracy reduction on question answering with fine-tuned RoBERTa.

technical paper

Following each patient visit, 
physicians draft long semi-structured
clinical summaries called SOAP notes.
While invaluable to clinicians and researchers,
creating digital SOAP notes is burdensome,
contributing to physician burnout.
In this paper, we introduce 
the first complete pipelines
to leverage deep summarization models
to generate these notes 
based on transcripts of conversations 
between physicians and patients.
After exploring a spectrum of methods
across the extractive-abstractive spectrum,
we propose Cluster2Sent,
an algorithm that
(i) extracts important utterances
relevant to each summary section;
(ii) clusters together related utterances;
and then (iii) generates 
one summary sentence
per cluster. 
Cluster2Sent outperforms
its purely abstractive counterpart
by 8 ROUGE-1 points,
and produces significantly more 
factual and coherent sentences 
as assessed by expert human evaluators.
For reproducibility, 
we demonstrate similar benefits
on the publicly available AMI dataset. 
Our results speak to the benefits
of structuring summaries into sections 
and annotating supporting evidence 
when constructing summarization corpora.

Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques

We investigate the problem of Chinese Grammatical Error Correction (CGEC) and present a new framework named Tail-to-Tail (\textbf{TtT}) non-autoregressive sequence prediction to address the deep issues hidden in CGEC. Considering that most tokens are correct and can be conveyed directly from source to target, and the error positions can be estimated and corrected based on the bidirectional context information, thus we employ a BERT-initialized Transformer Encoder as the backbone model to conduct information modeling and conveying. Considering that only relying on the same position substitution cannot handle the variable-length correction cases, various operations such substitution, deletion, insertion, and local paraphrasing are required jointly. Therefore, a Conditional Random Fields (CRF) layer is stacked on the up tail to conduct non-autoregressive sequence prediction by modeling the token dependencies. Since most tokens are correct and easily to be predicted/conveyed to the target, then the models may suffer from a severe class imbalance issue. To alleviate this problem, focal loss penalty strategies are integrated into the loss functions. Moreover, besides the typical fix-length error correction datasets, we also construct a variable-length corpus to conduct experiments. Experimental results on standard datasets, especially on the variable-length datasets, demonstrate the effectiveness of TtT in terms of sentence-level Accuracy, Precision, Recall, and F1-Measure on tasks of error Detection and Correction.

Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction

An important risk that children face today is <em>online grooming</em>, where a so-called sexual predator establishes an emotional connection with a minor online with the objective of sexual abuse. Prior work has sought to automatically identify grooming chats, but only after an incidence has already happened in the context of legal prosecution. In this work, we instead investigate this problem from the point of view of prevention. We define and study the task of <em>early sexual predator detection</em> (eSPD) in chats, where the goal is to analyze a running chat from its beginning and predict grooming attempts as early and as accurately as possible. We survey existing datasets and their limitations regarding eSPD, and create a new dataset called PANC for more realistic evaluations. We present strong baselines built on BERT that also reach state-of-the-art results for conventional SPD. Finally, we consider coping with limited computational resources, as real-life applications require eSPD on mobile devices.

Early Detection of Sexual Predators in Chats

Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentence-level templates for clinically accurate report generation. MedWriter first employs the Visual-Language Retrieval (VLR) module to retrieve the most relevant reports for the given images. To guarantee the logical coherence between generated sentences, the Language-Language Retrieval (LLR) module is introduced to retrieve relevant sentences based on the previous generated description. At last, a language decoder fuses image features and features from retrieved reports and sentences to generate meaningful medical reports. We verified the effectiveness of our model by automatic evaluation and human evaluation on two datasets, i.e., Open-I and MIMIC-CXR.

Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation

An attention matrix of a transformer self-attention sublayer can provably be decomposed into two components and only one of them (effective attention) contributes to the model output. This leads us to ask whether visualizing effective attention gives different conclusions than interpretation of standard attention. Using a subset of the GLUE tasks and BERT, we carry out an analysis to compare the two attention matrices, and show that their interpretations differ. Effective attention is less associated with the features related to the language modeling pretraining such as the separator token, and it has more potential to illustrate linguistic features captured by the model for solving the end-task. Given the found differences, we recommend using effective attention for studying a transformer's behavior since it is more pertinent to the model output by design.

Effective Attention Sheds Light On Interpretability

We introduce a language modeling architecture which operates over sequences of images, or over multimodal sequences of images with associated labels. We use this architecture alongside other embedding models to investigate a category of signs called complex graphemes (CGs) in the undeciphered proto-Elamite script. We argue that CGs have meanings which are at least partly compositional, and we discover novel rules governing the construction of CGs. We find that a language model over sign images produces more interpretable results than a model over text or over sign images and text, which suggests that the names given to signs may be obscuring signals in the corpus. Our results reveal previously unknown regularities in proto-Elamite sign use that can inform future decipherment efforts, and our image-aware language model provides a novel way to abstract away from biases introduced by human annotators.

Compositionality of Complex Graphemes in the Undeciphered Proto-{E}lamite Script using Image and Text Embedding Models

Language understanding must identify the logical connections between events in a discourse, but core events are often unstated due to their commonsense nature. This paper fills in these missing events by generating precondition events. Precondition generation can be framed as a sequence-to-sequence problem: given a target event, generate a possible precondition. However, in most real-world scenarios, an event can have several preconditions, requiring diverse generation – a challenge for standard seq2seq approaches. We propose DiP, a Diverse Precondition generation system that can generate unique and diverse preconditions.
DiP uses a generative process with three components – an event sampler, a candidate generator, and a post-processor. The event sampler provides control codes (precondition triggers) which the candidate generator uses to focus its generation. Unlike other conditional generation systems, DiP automatically generates control codes without training on diverse examples. Analysis against baselines reveals that DiP improves the diversity of preconditions significantly while also generating more preconditions.

Toward Diverse Precondition

Many new books get published every year, and only a fraction of them become popular among the readers. So the prediction of a book success can be a very useful parameter for publishers to make a reliable decision. This article presents the study of semantic word associations using the word embedding of book content for a set of Roget’s thesaurus concepts for book success prediction. In this work, we discuss the method to represent a book as a spectrum of concepts based on the association score between its content embedding and a global embedding (i.e. fastText) for a set of semantically linked word clusters. We show that the semantic word associations outperform the previous methods for book success prediction. In addition, we present that semantic word associations also provide better results than using features like the frequency of word groups in Roget’s thesaurus, LIWC (a popular tool for linguistic inquiry and word count), NRC (word association emotion lexicon), and part of speech (PoS). Our study reports that concept associations based on Roget’s Thesaurus using word embedding of individual novel resulted in the state-of-the-art performance of 0.89 average weighted F1-score for book success prediction. Finally, we present a set of dominant themes that contribute towards the popularity of a book for a specific genre.

A Study on Using Semantic Word Associations to Predict the Success of a Novel

We present the first publicly available Farsi dataset for fact extraction and verification. We adopt the construction procedure of the standard English dataset for the task, i.e., FEVER, and improve it for the case of low resource languages. The dataset comprises 23 K manually annotated claims. Over 65 % of the claims in ParsFEVER are many-hop. Despite having a smaller training set (around one-ninth of that in Fever), a model trained on ParsFEVER attains similar downstream performance, indicating the quality of the dataset.

ParsFEVER : a Dataset for Farsi Fact Extraction and Verification

Prior research has explored the ability of computational models to predict a word semantic fit with a given predicate. While much work has been devoted to modeling the typicality relation between verbs and arguments in isolation, in this paper we take a broader perspective by assessing whether and to what extent computational approaches have access to the information about the typicality of entire events and situations described in language (Generalized Event Knowledge).
Given the recent success of Transformers Language Models (TLMs), we decided to test them on a benchmark for the dynamic estimation of thematic fit. The evaluation of these models was performed in comparison with SDM, a framework specifically designed to integrate events in sentence meaning representations, and we conducted a detailed error analysis to investigate which factors affect their behavior. Our results show that TLMs can reach performances that are comparable to those
achieved by SDM. However, additional analysis consistently suggests that TLMs do not capture important aspects of event knowledge, and their predictions often depend on surface linguistic features, such as frequent words, collocations and syntactic patterns, thereby showing sub-optimal generalization abilities.

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