Some questions about the trippy dst

[zqwerty]

I have several questions:

1. the original performance of trippy is 55.3% on multiwoz 2.1(in paper). Your bert-base DST achieves 56.3%. So where does the improvement comes from? I notice that the original trippy repo mentions:

   With a sequence length of 180, you should expect the following average JGA: 56% for MultiWOZ 2.1` Best performance can be achieved by using the maximum sequence length of 512.

   Do you change the code or just the trippy repo has better performance than trippy paper?

2. How to reproduce the dst experiments? I guess:

   • for BERT(56.3%): run DO.example.advanced without any modification (although the max seq length is set to 180)
   • for CONVBERT-DG(58.57%): run DO.example.advanced with --model_name_or_path="convbert-dg"

Look forward to your reply :)

[Shikib]

1. For the BERT-base results, we didn't change the code. We just trained the model for longer.

2. Yes, that is correct.

[zqwerty]

Thank you very much! I run the BERT script without -mlm_pre --mlm_during and got:

(Pdb) acc_list_s [('multiwoz_trippy/pred_res.test.final.json', 0.582067281606077)]

and the number for dev set is 0.623407

Is that the joint acc. for dst? If so, why it is higher than 56.3%?

[zqwerty]

How can I evaluate dialoglue locally (not submit to eval.ai)?

[Shikib]

That is strange. You didn't change anything else to get the 58.2 number? I'm not sure why that happens, in our experiments the highest we were able to get (with our set of hyperparameters) without MLM was 56.3.

The evaluation can be done by running https://github.com/alexa/dialoglue/blob/master/evaluate.py using gt_test.json as the test_annotation_file and your json as the user_annotation_file.

[zqwerty]

The script I used:

#!/bin/bash

# Parameters ------------------------------------------------------

#TASK="sim-m"
#DATA_DIR="data/simulated-dialogue/sim-M"
#TASK="sim-r"
#DATA_DIR="data/simulated-dialogue/sim-R"
#TASK="woz2"
#DATA_DIR="data/woz2"
TASK="multiwoz21"
DATA_DIR="data/MULTIWOZ2.1"

# Project paths etc. ----------------------------------------------

OUT_DIR=multiwoz_trippy/
mkdir -p ${OUT_DIR}

# Main ------------------------------------------------------------

for step in train dev test; do
    args_add=""
    if [ "$step" = "train" ]; then
    args_add="--do_train --predict_type=dummy"
    elif [ "$step" = "dev" ] || [ "$step" = "test" ]; then
    args_add="--do_eval --predict_type=${step}"
    fi

    CUDA_VISIBLE_DEVICES=7 python3 run_dst.py \
        --task_name=${TASK} \
        --data_dir=${DATA_DIR} \
        --dataset_config=dataset_config/${TASK}.json \
        --model_type="bert" \
        --model_name_or_path="/home/data/zhuqi/pre-trained-models/bert-base-uncased" \
        --do_lower_case \
        --learning_rate=1e-4 \
        --num_train_epochs=50 \
        --max_seq_length=180 \
        --per_gpu_train_batch_size=48 \
        --per_gpu_eval_batch_size=1 \
        --output_dir=${OUT_DIR} \
        --save_epochs=20 \
        --logging_steps=10 \
        --warmup_proportion=0.1 \
        --adam_epsilon=1e-6 \
        --label_value_repetitions \
            --swap_utterances \
        --append_history \
        --use_history_labels \
        --delexicalize_sys_utts \
        --class_aux_feats_inform \
        --class_aux_feats_ds \
        --seed 42 \
        ${args_add} \
        2>&1 | tee ${OUT_DIR}/${step}.log

    if [ "$step" = "dev" ] || [ "$step" = "test" ]; then
        python3 metric_bert_dst.py \
            ${TASK} \
        dataset_config/${TASK}.json \
            "${OUT_DIR}/pred_res.${step}*json" \
            2>&1 | tee ${OUT_DIR}/eval_pred_${step}.log
    fi
done

I copy the multiwoz data from dialoglue/multiwoz to data/MULTIWOZ2.1, add mkdir -p ${OUT_DIR}, remove --mlm_pre --mlm_during. GPU: Quadro RTX 6000

Here is the pred.log: eval_pred_dev.log eval_pred_test.log

[zqwerty]

What is the hyperparameter setting for few-shot experiments?

[Shikib]

That appears to be identical to our script. I am not sure why the performance is so high.

The hyperparameter for the few shot setting is identical, we just run the experiment 5x (with random seeds = 1 - 5).

[mihail-amazon]

I'm going to close this issue for now, but feel free to open a new one if you have any additional questions.

[zqwerty]

That appears to be identical to our script. I am not sure why the performance is so high.

The hyperparameter for the few shot setting is identical, we just run the experiment 5x (with random seeds = 1 - 5).

Seems the number in paper for few-shot dst is not correct. I've got ~48 for bert